1. IMPORT ALL LIBRARIES¶

In [ ]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from sklearn.metrics import mean_absolute_error, r2_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler

2. LOAD THE DATASET¶

In [ ]:
# Read datasets
data = pd.read_csv('../Monthly and Annualy/Jimma  Station Monthly Climate Data 1993-2022.csv')
data.head()
Out[ ]:
Parameters Year JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual
0 Relative Humidity 1993 77.69 73.94 67.56 84.06 86.81 90.94 90.12 88.38 85.50 84.31 71.25 64.75 80.44
1 Relative Humidity 1994 54.50 55.50 71.06 79.62 86.12 90.62 91.19 89.62 86.94 77.12 74.69 66.62 77.06
2 Relative Humidity 1995 57.06 68.19 73.44 81.56 85.44 88.44 90.44 89.56 88.50 84.00 72.12 75.62 79.56
3 Relative Humidity 1996 76.88 67.81 78.12 83.94 85.56 90.25 90.75 89.88 89.94 82.94 73.56 68.50 81.56
4 Relative Humidity 1997 71.81 49.19 64.62 84.62 86.94 89.75 89.81 88.06 87.00 87.50 85.12 78.31 80.44
In [ ]:
# rename the parameter
data.rename(columns={'Parameters':'Parameter', 'Year':'YEAR'}, inplace=True)
data.head()
Out[ ]:
Parameter YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual
0 Relative Humidity 1993 77.69 73.94 67.56 84.06 86.81 90.94 90.12 88.38 85.50 84.31 71.25 64.75 80.44
1 Relative Humidity 1994 54.50 55.50 71.06 79.62 86.12 90.62 91.19 89.62 86.94 77.12 74.69 66.62 77.06
2 Relative Humidity 1995 57.06 68.19 73.44 81.56 85.44 88.44 90.44 89.56 88.50 84.00 72.12 75.62 79.56
3 Relative Humidity 1996 76.88 67.81 78.12 83.94 85.56 90.25 90.75 89.88 89.94 82.94 73.56 68.50 81.56
4 Relative Humidity 1997 71.81 49.19 64.62 84.62 86.94 89.75 89.81 88.06 87.00 87.50 85.12 78.31 80.44

3. TRANSFORM THE DATASET¶

In [ ]:
def transform_data(df):
    melted_df = df.melt(id_vars=["Parameter", "YEAR"],
                        value_vars=["JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC"],
                        var_name="Month", value_name="Value")
    month_mapping = {
        "JAN": "01", "FEB": "02", "MAR": "03", "APR": "04", "MAY": "05", "JUN": "06",
        "JUL": "07", "AUG": "08", "SEP": "09", "OCT": "10", "NOV": "11", "DEC": "12"
    }
    melted_df["Month"] = melted_df["Month"].map(month_mapping)
    melted_df["Date"] = pd.to_datetime(melted_df["YEAR"].astype(str) + "-" + melted_df["Month"] + "-01")
    transformed_df = melted_df[["Parameter", "Date", "Value"]]
    # print(pd.DataFrame(transformed_df))
    return transformed_df
In [ ]:
# Transform the data
transformed_data = transform_data(data)
In [ ]:
transformed_data.head()
Out[ ]:
Parameter Date Value
0 Relative Humidity 1993-01-01 77.69
1 Relative Humidity 1994-01-01 54.50
2 Relative Humidity 1995-01-01 57.06
3 Relative Humidity 1996-01-01 76.88
4 Relative Humidity 1997-01-01 71.81
In [ ]:
# UNIQUE VALUES ONLY
unique_parameters = transformed_data['Parameter'].unique()
In [ ]:
# RENAMA THE PARAMETERS
rename_dict = {
    unique_parameters[0]: 'Relative Humidity',
    unique_parameters[1]: 'Max Temperature',
    unique_parameters[2]: 'Min Temperature',
    unique_parameters[3]: 'Max Windspeed',
    unique_parameters[4]: 'Min Windspeed',
    unique_parameters[5]: 'Precipitation',
    unique_parameters[6]: 'Solar Radiation'
}

# Renaming the parameters
transformed_data['Parameter'] = transformed_data['Parameter'].replace(rename_dict)
transformed_data.head()
Out[ ]:
Parameter Date Value
0 Relative Humidity 1993-01-01 77.69
1 Relative Humidity 1994-01-01 54.50
2 Relative Humidity 1995-01-01 57.06
3 Relative Humidity 1996-01-01 76.88
4 Relative Humidity 1997-01-01 71.81
In [ ]:
# Parameter counts
transformed_data.Parameter.value_counts()
Out[ ]:
Parameter
Relative Humidity    360
Max Temperature      360
Min Temperature      360
Max Windspeed        360
Min Windspeed        360
Precipitation        360
Solar Radiation      360
Name: count, dtype: int64

4. PLOT VALUES OF EACH PARAMETERS¶

In [ ]:
parameters = transformed_data['Parameter'].unique()
parameters
Out[ ]:
array(['Relative Humidity', 'Max Temperature', 'Min Temperature',
       'Max Windspeed', 'Min Windspeed', 'Precipitation',
       'Solar Radiation'], dtype=object)
In [ ]:
# Temperature, max and min
temp_param = parameters[1:3]
wind_param = parameters[3:5]
In [ ]:
temp_custom_colors = {
    temp_param[0]: '#8b0000',
    temp_param[1]: '#ff6347',
}

plt.figure(figsize=(40, 10))

for parameter in temp_param:
    parameter_data = transformed_data[transformed_data['Parameter'] == parameter]
    parameter_data.set_index('Date', inplace=True)
    sns.lineplot(data=parameter_data, 
                 x=parameter_data.index, 
                 y='Value', 
                 marker='o', 
                 label=parameter, 
                 linewidth=1, 
                 color=temp_custom_colors[parameter])

# plt.title('Monthly Analysis', fontsize=24)
plt.xlabel('Date', fontsize=24)
plt.ylabel('Temperature(°C)', fontsize=24)
plt.legend(loc='best', fontsize=24, framealpha=0.5) 
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.show()
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In [ ]:
wind_custom_colors = {
    wind_param[0]: '#8c564b',
    wind_param[1]: '#ff7f0e',
}

plt.figure(figsize=(40, 10))

for parameter in wind_param:
    parameter_data = transformed_data[transformed_data['Parameter'] == parameter]
    parameter_data.set_index('Date', inplace=True)
    sns.lineplot(data=parameter_data, 
                 x=parameter_data.index, 
                 y='Value', 
                 marker='o', 
                 label=parameter, 
                 linewidth=1, 
                 color=wind_custom_colors[parameter])

# plt.title('Monthly Analysis', fontsize=24)
plt.xlabel('Date', fontsize=24)
plt.ylabel('Windspeed (m/s)', fontsize=24)
plt.legend(loc='best', fontsize=24, framealpha=0.5) 
plt.xticks(fontsize=24)
plt.yticks(fontsize=24)
plt.show()
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5. FIND THE CORRELATIONS BETWEEN EACH PARAMETERS¶

In [ ]:
pivot_data = transformed_data.pivot(index='Date', columns='Parameter', values='Value')
correlation_matrix = pivot_data.corr()

plt.figure(figsize=(16, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', square=True, linewidths=0.5, annot_kws={"size": 18})

plt.xticks(rotation=90, fontsize=20)
plt.yticks(rotation=0, fontsize=20)

plt.xlabel('')
plt.ylabel('')
plt.show()
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6. COMMON FUNCTION¶

TIME SERIES TO SUPERVISED¶

In [ ]:
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = pd.DataFrame(data)
    cols = []
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
    for i in range(0, n_out):
        cols.append(df.shift(-i))
    agg = pd.concat(cols, axis=1)
    if dropnan:
        agg.dropna(inplace=True)
    return agg.values

ADD ROLLING FEATURES¶

In [ ]:
def add_rolling_features(data, window=3):
    df = pd.DataFrame(data)
    df['rolling_mean'] = df.iloc[:, 0].rolling(window=window).mean()
    df['rolling_std'] = df.iloc[:, 0].rolling(window=window).std()
    df.dropna(inplace=True)
    return df

TRAIN TEST SPLITT¶

In [ ]:
def train_test_split(data, train_size=0.9):
    n_train = int(len(data) * train_size)
    return data[:n_train], data[n_train:]

MEASURING METRICS¶

In [ ]:
def nash_sutcliffe_efficiency(y_true, y_pred):
    return 1 - (np.sum((y_true - y_pred)**2) / np.sum((y_true - np.mean(y_true))**2))
def willmotts_index(y_true, y_pred):
    return 1 - (np.sum((y_pred - y_true)**2) / np.sum((np.abs(y_pred - np.mean(y_true)) + np.abs(y_true - np.mean(y_true)))**2))

CROSS VALIDATION AND MODEL EVALUATIONS¶

In [ ]:
def walk_forward_validation(data, model):
    predictions = []
    train, test = train_test_split(data)
    history = [x for x in train.values]
    for i in range(len(test)):
        testX, testy = test.iloc[i, :-1].values, test.iloc[i, -1]
        yhat = model(history, testX)
        predictions.append(yhat)
        history.append(test.iloc[i].values)
        print('>expected=%.1f, predicted=%.1f' % (testy, yhat))
    mae = mean_absolute_error(test.iloc[:, -1], predictions)
    rmse = np.sqrt(np.mean((test.iloc[:, -1] - np.asarray(predictions))**2))
    r2 = r2_score(test.iloc[:, -1], predictions)
    nse = nash_sutcliffe_efficiency(test.iloc[:, -1], predictions)
    willmott = willmotts_index(test.iloc[:, -1], predictions)
    return mae, rmse, r2, nse, willmott, test.index, test.iloc[:, -1], predictions

7. MODELS¶

1. RANDOM FOREST¶

In [ ]:
def random_forest_forecast(train, testX):
    train = np.asarray(train)
    trainX, trainy = train[:, :-1], train[:, -1]
    model = RandomForestRegressor(n_estimators=50)
    model.fit(trainX, trainy)
    yhat = model.predict([testX])
    return yhat[0]

2. SVM¶

In [ ]:
from sklearn.svm import SVR
def svm_forecast(train, testX):
    train = np.asarray(train)
    trainX, trainy = train[:, :-1], train[:, -1]
    model = SVR(kernel='rbf')
    model.fit(trainX, trainy)
    yhat = model.predict([testX])
    return yhat[0]

3. XGBRegressor¶

In [ ]:
from xgboost import XGBRegressor
def xgboost_forecast(train, testX):
    train = np.asarray(train)
    trainX, trainy = train[:, :-1], train[:, -1]
    model = XGBRegressor(n_estimators=50)
    model.fit(trainX, trainy)
    yhat = model.predict([testX])
    return yhat[0]

4. LGBMRegressor¶

In [ ]:
from lightgbm import LGBMRegressor
def lightgbm_forecast(train, testX):
    train = np.asarray(train)
    trainX, trainy = train[:, :-1], train[:, -1]
    model = LGBMRegressor(n_estimators=50, verbose=-1)
    model.fit(trainX, trainy)
    yhat = model.predict([testX])
    return yhat[0]
In [ ]:
# Color dictionary for different models
colors_dict = {
    'LightGBM': '#27ad81',
    'XGBoost': '#5dc863',
    'SVM': '#aadc32',
    'Random Forest': '#fde725'
}

6. HUMIDITY ANALYSIS¶

In [ ]:
# Get the humidity values
humidity = transformed_data[transformed_data['Parameter'] == parameters[0]]
humidity.head()
Out[ ]:
Parameter Date Value
0 Relative Humidity 1993-01-01 77.69
1 Relative Humidity 1994-01-01 54.50
2 Relative Humidity 1995-01-01 57.06
3 Relative Humidity 1996-01-01 76.88
4 Relative Humidity 1997-01-01 71.81
In [ ]:
# drop the parameter columns
humidity = humidity.drop(columns=['Parameter'])
In [ ]:
humidity.head()
Out[ ]:
Date Value
0 1993-01-01 77.69
1 1994-01-01 54.50
2 1995-01-01 57.06
3 1996-01-01 76.88
4 1997-01-01 71.81
In [ ]:
# sort according to  the date
humidity= humidity.sort_values(by='Date')
humidity.head()
Out[ ]:
Date Value
0 1993-01-01 77.69
210 1993-02-01 73.94
420 1993-03-01 67.56
630 1993-04-01 84.06
840 1993-05-01 86.81
In [ ]:
humidity.head()
Out[ ]:
Date Value
0 1993-01-01 77.69
210 1993-02-01 73.94
420 1993-03-01 67.56
630 1993-04-01 84.06
840 1993-05-01 86.81
In [ ]:
plt.figure(figsize=(20, 8))
humidity.plot(x='Date', y='Value', kind='line', ax=plt.gca(), color='#2ca02c', fontsize=12)
plt.xlabel('Date', fontdict={'fontsize':20})
plt.ylabel('Relative Humidity (g/m³ and g/kg)', fontdict={'fontsize':20})
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.grid(linestyle='--')
plt.tight_layout()
plt.legend().remove()
plt.show()
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In [ ]:
# Add fuller test to check the stationarity of the dataset
def adfuller_test(values):
    result = adfuller(values)
    labels = ['ADF Test Statistic','p-value','#Lags Used', 'Number of Observations Used']
    for value, label in zip(result, labels):
        print(label+' : '+str(value))
    if result[1] <= 0.05:
        print("Strong evidence against the null hypothesis(H0)")
    else:
        print("Weak evidence against the null hypothesis(H0)")
In [ ]:
# H0: It is non stationary
# H1: It is stationary
In [ ]:
adfuller_test(humidity['Value'])

ADF Test Statistic : -3.9421966060686677
p-value : 0.0017453416022382069
#Lags Used : 13
Number of Observations Used : 346
Strong evidence against the null hypothesis(H0)
In [ ]:
humidity = humidity[['Date', 'Value']]
humidity.set_index('Date', inplace=True)
humidity.head()
Out[ ]:
Value
Date
1993-01-01 77.69
1993-02-01 73.94
1993-03-01 67.56
1993-04-01 84.06
1993-05-01 86.81
In [ ]:
model_names = []
mae_values = []
rmse_values = []
r2_values = []
nse_values = []
willmott_values = []

TRAIN THE MODEL¶

In [ ]:
# Assuming `humidity` is your time series DataFrame
series = humidity
values = series.values
values = add_rolling_features(values, window=3)
values = pd.DataFrame(values).values
scaler = StandardScaler()
values = scaler.fit_transform(values)
data = series_to_supervised(values, n_in=6)
data_df = pd.DataFrame(data, index=series.index[6+2:])
In [ ]:
# Model names
models = [
    ('LightGBM', lightgbm_forecast),
    ('XGBoost', xgboost_forecast),
    ('SVM', svm_forecast),
    ('Random Forest', random_forest_forecast)
]

# Clear existing lists in case they contain previous values
model_names.clear()
mae_values.clear()
rmse_values.clear()
r2_values.clear()
nse_values.clear()
willmott_values.clear()

# Iterate over each model, perform walk-forward validation, and append metrics to lists
for model_name, model_func in models:
    mae, rmse, r2, nse, willmott, test_index, y, yhat = walk_forward_validation(data_df, model_func)
    
    model_names.append(model_name)
    mae_values.append(mae)
    rmse_values.append(rmse)
    r2_values.append(r2)
    nse_values.append(nse)
    willmott_values.append(willmott)

# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics_df = pd.DataFrame(metrics)

# Plot the actual vs predicted values for each model
plt.figure(figsize=(16, 6))
plt.plot(test_index, y, label='Actual', linestyle='--', color='gray', linewidth=2)

# Plot predictions for each model
for model_name, model_func in models:
    _, _, _, _, _, test_index, _, yhat = walk_forward_validation(data_df, model_func)
    plt.plot(test_index, yhat, label=f'{model_name} Predicted', linestyle='--', color=colors_dict[model_name], linewidth=2)

plt.xlabel('Year and Month', fontsize=18)
plt.ylabel('Scaled Relative Humidity (g/m³ and g/kg)', fontsize=18)
plt.legend(fontsize=14, loc='upper center', bbox_to_anchor=(0.6, 1.15), ncol=1)
plt.xticks(rotation=45, fontsize=14)
plt.yticks(fontsize=14)

plt.grid(axis='y', linestyle='--')

ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.3
>expected=-0.7, predicted=-0.5
>expected=-0.3, predicted=0.3
>expected=0.2, predicted=0.3
>expected=0.2, predicted=-0.2
>expected=-0.8, predicted=-0.7
>expected=-1.2, predicted=-1.2
>expected=-1.1, predicted=-1.2
>expected=0.4, predicted=0.1
>expected=0.6, predicted=0.6
>expected=0.4, predicted=0.3
>expected=-0.1, predicted=-0.3
>expected=-1.0, predicted=-0.5
>expected=0.7, predicted=0.7
>expected=1.0, predicted=0.8
>expected=2.6, predicted=1.9
>expected=0.8, predicted=0.5
>expected=-0.7, predicted=-0.6
>expected=-0.8, predicted=-0.9
>expected=-1.2, predicted=-1.2
>expected=-0.2, predicted=-0.2
>expected=0.5, predicted=0.3
>expected=0.1, predicted=0.1
>expected=-0.6, predicted=-0.3
>expected=-0.7, predicted=-0.1
>expected=-0.1, predicted=0.7
>expected=0.5, predicted=0.6
>expected=1.2, predicted=1.7
>expected=0.6, predicted=0.6
>expected=0.3, predicted=-0.1
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-1.2
>expected=-0.7, predicted=-0.8
>expected=0.5, predicted=0.4
>expected=0.3, predicted=0.6
>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.5
>expected=-0.7, predicted=-0.5
>expected=-0.3, predicted=0.1
>expected=0.2, predicted=0.5
>expected=0.2, predicted=0.0
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.1
>expected=-1.1, predicted=-1.1
>expected=0.4, predicted=0.2
>expected=0.6, predicted=0.6
>expected=0.4, predicted=0.4
>expected=-0.1, predicted=-0.5
>expected=-1.0, predicted=-0.3
>expected=0.7, predicted=0.4
>expected=1.0, predicted=0.9
>expected=2.6, predicted=2.6
>expected=0.8, predicted=0.8
>expected=-0.7, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.1
>expected=-0.2, predicted=-0.3
>expected=0.5, predicted=0.4
>expected=0.1, predicted=0.3
>expected=-0.6, predicted=-0.2
>expected=-0.7, predicted=-0.2
>expected=-0.1, predicted=0.1
>expected=0.5, predicted=0.6
>expected=1.2, predicted=2.2
>expected=0.6, predicted=0.6
>expected=0.3, predicted=0.1
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-0.7, predicted=-0.6
>expected=0.5, predicted=0.5
>expected=0.3, predicted=0.5
>expected=-0.5, predicted=-0.5
>expected=-0.4, predicted=-0.1
>expected=-0.7, predicted=-0.2
>expected=-0.3, predicted=-0.0
>expected=0.2, predicted=0.1
>expected=0.2, predicted=-0.1
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.3
>expected=-1.1, predicted=-1.1
>expected=0.4, predicted=-0.0
>expected=0.6, predicted=0.7
>expected=0.4, predicted=0.4
>expected=-0.1, predicted=-0.1
>expected=-1.0, predicted=-0.2
>expected=0.7, predicted=-0.1
>expected=1.0, predicted=1.2
>expected=2.6, predicted=2.4
>expected=0.8, predicted=0.7
>expected=-0.7, predicted=-0.6
>expected=-0.8, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=-0.2, predicted=-0.7
>expected=0.5, predicted=0.3
>expected=0.1, predicted=0.5
>expected=-0.6, predicted=-0.3
>expected=-0.7, predicted=-0.2
>expected=-0.1, predicted=0.1
>expected=0.5, predicted=1.0
>expected=1.2, predicted=1.7
>expected=0.6, predicted=0.7
>expected=0.3, predicted=-0.0
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-1.2
>expected=-0.7, predicted=-0.8
>expected=0.5, predicted=0.3
>expected=0.3, predicted=0.5
>expected=-0.5, predicted=-0.4
>expected=-0.4, predicted=-0.5
>expected=-0.7, predicted=-0.2
>expected=-0.3, predicted=0.3
>expected=0.2, predicted=0.3
>expected=0.2, predicted=-0.3
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.2
>expected=-1.1, predicted=-1.1
>expected=0.4, predicted=0.2
>expected=0.6, predicted=0.5
>expected=0.4, predicted=0.3
>expected=-0.1, predicted=-0.1
>expected=-1.0, predicted=-0.2
>expected=0.7, predicted=0.3
>expected=1.0, predicted=0.7
>expected=2.6, predicted=2.5
>expected=0.8, predicted=0.7
>expected=-0.7, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.2
>expected=-0.2, predicted=-0.5
>expected=0.5, predicted=0.4
>expected=0.1, predicted=0.1
>expected=-0.6, predicted=0.1
>expected=-0.7, predicted=-0.0
>expected=-0.1, predicted=0.4
>expected=0.5, predicted=0.8
>expected=1.2, predicted=1.1
>expected=0.6, predicted=0.6
>expected=0.3, predicted=-0.1
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-1.2
>expected=-0.7, predicted=-0.6
>expected=0.5, predicted=0.4
>expected=0.3, predicted=0.5
>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.3
>expected=-0.7, predicted=-0.5
>expected=-0.3, predicted=0.3
>expected=0.2, predicted=0.3
>expected=0.2, predicted=-0.2
>expected=-0.8, predicted=-0.7
>expected=-1.2, predicted=-1.2
>expected=-1.1, predicted=-1.2
>expected=0.4, predicted=0.1
>expected=0.6, predicted=0.6
>expected=0.4, predicted=0.3
>expected=-0.1, predicted=-0.3
>expected=-1.0, predicted=-0.5
>expected=0.7, predicted=0.7
>expected=1.0, predicted=0.8
>expected=2.6, predicted=1.9
>expected=0.8, predicted=0.5
>expected=-0.7, predicted=-0.6
>expected=-0.8, predicted=-0.9
>expected=-1.2, predicted=-1.2
>expected=-0.2, predicted=-0.2
>expected=0.5, predicted=0.3
>expected=0.1, predicted=0.1
>expected=-0.6, predicted=-0.3
>expected=-0.7, predicted=-0.1
>expected=-0.1, predicted=0.7
>expected=0.5, predicted=0.6
>expected=1.2, predicted=1.7
>expected=0.6, predicted=0.6
>expected=0.3, predicted=-0.1
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-1.2
>expected=-0.7, predicted=-0.8
>expected=0.5, predicted=0.4
>expected=0.3, predicted=0.6
>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.5
>expected=-0.7, predicted=-0.5
>expected=-0.3, predicted=0.1
>expected=0.2, predicted=0.5
>expected=0.2, predicted=0.0
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.1
>expected=-1.1, predicted=-1.1
>expected=0.4, predicted=0.2
>expected=0.6, predicted=0.6
>expected=0.4, predicted=0.4
>expected=-0.1, predicted=-0.5
>expected=-1.0, predicted=-0.3
>expected=0.7, predicted=0.4
>expected=1.0, predicted=0.9
>expected=2.6, predicted=2.6
>expected=0.8, predicted=0.8
>expected=-0.7, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.1
>expected=-0.2, predicted=-0.3
>expected=0.5, predicted=0.4
>expected=0.1, predicted=0.3
>expected=-0.6, predicted=-0.2
>expected=-0.7, predicted=-0.2
>expected=-0.1, predicted=0.1
>expected=0.5, predicted=0.6
>expected=1.2, predicted=2.2
>expected=0.6, predicted=0.6
>expected=0.3, predicted=0.1
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-0.7, predicted=-0.6
>expected=0.5, predicted=0.5
>expected=0.3, predicted=0.5
>expected=-0.5, predicted=-0.5
>expected=-0.4, predicted=-0.1
>expected=-0.7, predicted=-0.2
>expected=-0.3, predicted=-0.0
>expected=0.2, predicted=0.1
>expected=0.2, predicted=-0.1
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.3
>expected=-1.1, predicted=-1.1
>expected=0.4, predicted=-0.0
>expected=0.6, predicted=0.7
>expected=0.4, predicted=0.4
>expected=-0.1, predicted=-0.1
>expected=-1.0, predicted=-0.2
>expected=0.7, predicted=-0.1
>expected=1.0, predicted=1.2
>expected=2.6, predicted=2.4
>expected=0.8, predicted=0.7
>expected=-0.7, predicted=-0.6
>expected=-0.8, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=-0.2, predicted=-0.7
>expected=0.5, predicted=0.3
>expected=0.1, predicted=0.5
>expected=-0.6, predicted=-0.3
>expected=-0.7, predicted=-0.2
>expected=-0.1, predicted=0.1
>expected=0.5, predicted=1.0
>expected=1.2, predicted=1.7
>expected=0.6, predicted=0.7
>expected=0.3, predicted=-0.0
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-1.2
>expected=-0.7, predicted=-0.8
>expected=0.5, predicted=0.3
>expected=0.3, predicted=0.5
>expected=-0.5, predicted=-0.4
>expected=-0.4, predicted=-0.5
>expected=-0.7, predicted=-0.2
>expected=-0.3, predicted=0.5
>expected=0.2, predicted=0.3
>expected=0.2, predicted=-0.2
>expected=-0.8, predicted=-0.9
>expected=-1.2, predicted=-1.2
>expected=-1.1, predicted=-1.1
>expected=0.4, predicted=0.1
>expected=0.6, predicted=0.5
>expected=0.4, predicted=0.5
>expected=-0.1, predicted=-0.2
>expected=-1.0, predicted=-0.4
>expected=0.7, predicted=0.4
>expected=1.0, predicted=0.4
>expected=2.6, predicted=2.2
>expected=0.8, predicted=0.6
>expected=-0.7, predicted=-0.5
>expected=-0.8, predicted=-0.8
>expected=-1.2, predicted=-1.2
>expected=-0.2, predicted=-0.5
>expected=0.5, predicted=0.2
>expected=0.1, predicted=0.3
>expected=-0.6, predicted=-0.0
>expected=-0.7, predicted=-0.2
>expected=-0.1, predicted=0.2
>expected=0.5, predicted=0.6
>expected=1.2, predicted=1.4
>expected=0.6, predicted=0.5
>expected=0.3, predicted=-0.1
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-1.2
>expected=-0.7, predicted=-0.7
>expected=0.5, predicted=0.4
>expected=0.3, predicted=0.5
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EVALUATE THE MODEL¶

In [ ]:
# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics = pd.DataFrame(metrics)
In [ ]:
metrics
Out[ ]:
Model MAE RMSE R-squared Nash-Sutcliffe Efficiency Willmott's Index of Agreement
0 LightGBM 0.225678 0.311640 0.854801 0.854801 0.959747
1 XGBoost 0.180423 0.278050 0.884415 0.884415 0.971010
2 SVM 0.246888 0.320127 0.846784 0.846784 0.959613
3 Random Forest 0.208909 0.303418 0.862361 0.862361 0.961649
In [ ]:
metrics = metrics.round(2)

fig, ax = plt.subplots(figsize=(20, 4))
ax.axis('off')

table = ax.table(cellText=metrics.values,
                 colLabels=metrics.columns,
                 cellLoc='center',
                 loc='center',
                 bbox=[0, 0, 1, 1])

table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.2, 1.2)

header_color = '#D0D0D0'
data_color = '#FFFFFF'

for i, key in enumerate(table._cells):
    cell = table._cells[key]
    cell.set_edgecolor('black')
    if key[0] == 0 or key[1] == -1:
        cell.set_text_props(weight='bold')
        cell.set_facecolor(header_color)
    else:
        cell.set_facecolor(data_color)

plt.title('Models Performance on Relative Humidity')
plt.show()
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In [ ]:
colors = ['#27ad81', '#5dc863', '#aadc32', '#fde725']
bar_width = 0.4

# Metrics and Titles
metrics_title = {
    'MAE': 'Mean Absolute Error (MAE)',
    'RMSE': 'Root Mean Squared Error (RMSE)',
    'R-squared': 'R-squared',
    'Nash-Sutcliffe Efficiency': 'Nash-Sutcliffe Efficiency',
    'Willmott\'s Index of Agreement': 'Willmott\'s Index of Agreement'
}

# Plot each metric separately
for metric, title in metrics_title.items():
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.bar(metrics['Model'], metrics[metric], color=colors, width=bar_width)
    # ax.set_title(title, fontsize=16)
    ax.set_ylabel(metric, fontsize=16)
    ax.tick_params(axis='x', rotation=30, labelsize=16)
    ax.tick_params(axis='y', labelsize=16)
    plt.tight_layout()
    plt.show()
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7. MAX TEMPERATURE ANALYSIS¶

In [ ]:
temp_max = transformed_data[transformed_data['Parameter'] == parameters[1]]
In [ ]:
# drop the parameter columns
temp_max = temp_max.drop(columns=['Parameter'])
In [ ]:
temp_max.head()
Out[ ]:
Date Value
30 1993-01-01 23.64
31 1994-01-01 29.11
32 1995-01-01 27.24
33 1996-01-01 23.06
34 1997-01-01 25.57
In [ ]:
# sort according to  the date
temp_max= temp_max.sort_values(by='Date')
temp_max.head()
Out[ ]:
Date Value
30 1993-01-01 23.64
240 1993-02-01 26.08
450 1993-03-01 26.67
660 1993-04-01 24.79
870 1993-05-01 23.94
In [ ]:
temp_max.head()
Out[ ]:
Date Value
30 1993-01-01 23.64
240 1993-02-01 26.08
450 1993-03-01 26.67
660 1993-04-01 24.79
870 1993-05-01 23.94
In [ ]:
plt.figure(figsize=(20, 8))
temp_max.plot(x='Date', y='Value', kind='line', ax=plt.gca(), color='#8b0000', fontsize=12)
plt.xlabel('Date', fontdict={'fontsize':20})
plt.ylabel('Max Temperature (°C)', fontdict={'fontsize':20})
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.grid(linestyle='--')
plt.tight_layout()
plt.legend().remove()
plt.show()
No description has been provided for this image
In [ ]:
# Add fuller test to check the stationarity of the dataset
def adfuller_test(values):
    result = adfuller(values)
    labels = ['ADF Test Statistic','p-value','#Lags Used', 'Number of Observations Used']
    for value, label in zip(result, labels):
        print(label+' : '+str(value))
    if result[1] <= 0.05:
        print("Strong evidence against the null hypothesis(H0)")
    else:
        print("Weak evidence against the null hypothesis(H0)")
In [ ]:
# H0: It is non stationary
# H1: It is stationary
In [ ]:
adfuller_test(temp_max['Value'])

ADF Test Statistic : -2.4950095023206686
p-value : 0.11669329339930473
#Lags Used : 13
Number of Observations Used : 346
Weak evidence against the null hypothesis(H0)
In [ ]:
temp_max = temp_max[['Date', 'Value']]
temp_max.set_index('Date', inplace=True)
temp_max.head()
Out[ ]:
Value
Date
1993-01-01 23.64
1993-02-01 26.08
1993-03-01 26.67
1993-04-01 24.79
1993-05-01 23.94

TRAIN THE MODEL¶

In [ ]:
# Assuming `temp_max` is your time series DataFrame
series = temp_max
values = series.values
values = add_rolling_features(values, window=3)
values = pd.DataFrame(values).values
scaler = StandardScaler()
values = scaler.fit_transform(values)
data = series_to_supervised(values, n_in=6)
data_df = pd.DataFrame(data, index=series.index[6+2:])
In [ ]:
# Model names
models = [
    ('LightGBM', lightgbm_forecast),
    ('XGBoost', xgboost_forecast),
    ('SVM', svm_forecast),
    ('Random Forest', random_forest_forecast)
]

# Clear existing lists in case they contain previous values
model_names.clear()
mae_values.clear()
rmse_values.clear()
r2_values.clear()
nse_values.clear()
willmott_values.clear()

# Iterate over each model, perform walk-forward validation, and append metrics to lists
for model_name, model_func in models:
    mae, rmse, r2, nse, willmott, test_index, y, yhat = walk_forward_validation(data_df, model_func)
    
    model_names.append(model_name)
    mae_values.append(mae)
    rmse_values.append(rmse)
    r2_values.append(r2)
    nse_values.append(nse)
    willmott_values.append(willmott)

# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics_df = pd.DataFrame(metrics)

# Plot the actual vs predicted values for each model
plt.figure(figsize=(16, 6))
plt.plot(test_index, y, label='Actual', linestyle='--', color='gray', linewidth=2)



# Plot predictions for each model
for model_name, model_func in models:
    _, _, _, _, _, test_index, _, yhat = walk_forward_validation(data_df, model_func)
    plt.plot(test_index, yhat, label=f'{model_name} Predicted', linestyle='--', color=colors_dict[model_name], linewidth=2)

plt.xlabel('Year and Month', fontsize=18)
plt.ylabel('Scaled Max Temperature (°C)', fontsize=18)
plt.legend(fontsize=14, loc='upper center', bbox_to_anchor=(0.6, 1.15), ncol=1)
plt.xticks(rotation=45, fontsize=14)
plt.yticks(fontsize=14)

plt.grid(axis='y', linestyle='--')

ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

>expected=-0.0, predicted=0.2
>expected=1.6, predicted=1.4
>expected=1.2, predicted=0.8
>expected=-0.5, predicted=-0.1
>expected=1.6, predicted=1.6
>expected=1.8, predicted=2.0
>expected=-0.1, predicted=-0.3
>expected=-1.1, predicted=-1.0
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-0.9
>expected=0.0, predicted=-0.1
>expected=0.6, predicted=0.5
>expected=0.0, predicted=-0.0
>expected=0.7, predicted=0.4
>expected=0.3, predicted=0.2
>expected=1.8, predicted=0.7
>expected=2.1, predicted=2.0
>expected=0.1, predicted=0.9
>expected=-0.2, predicted=-0.3
>expected=-1.2, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-0.9
>expected=-0.1, predicted=0.0
>expected=0.5, predicted=0.4
>expected=0.5, predicted=0.8
>expected=0.7, predicted=0.4
>expected=0.1, predicted=0.1
>expected=-0.0, predicted=0.2
>expected=2.9, predicted=2.0
>expected=2.3, predicted=2.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-1.0, predicted=-0.9
>expected=-0.4, predicted=-0.3
>expected=-0.0, predicted=-0.0
>expected=1.6, predicted=1.2
>expected=1.2, predicted=0.7
>expected=-0.5, predicted=-0.5
>expected=1.6, predicted=1.6
>expected=1.8, predicted=2.0
>expected=-0.1, predicted=-0.0
>expected=-1.1, predicted=-1.0
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-1.0
>expected=0.0, predicted=0.1
>expected=0.6, predicted=0.6
>expected=0.0, predicted=-0.0
>expected=0.7, predicted=0.8
>expected=0.3, predicted=0.3
>expected=1.8, predicted=1.1
>expected=2.1, predicted=1.5
>expected=0.1, predicted=1.1
>expected=-0.2, predicted=-0.3
>expected=-1.2, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=-0.2
>expected=0.5, predicted=0.5
>expected=0.5, predicted=1.0
>expected=0.7, predicted=0.7
>expected=0.1, predicted=0.4
>expected=-0.0, predicted=0.2
>expected=2.9, predicted=2.6
>expected=2.3, predicted=1.8
>expected=-0.9, predicted=-0.6
>expected=-0.9, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-0.4, predicted=-0.7
>expected=-0.0, predicted=0.1
>expected=1.6, predicted=1.5
>expected=1.2, predicted=1.0
>expected=-0.5, predicted=-0.0
>expected=1.6, predicted=1.3
>expected=1.8, predicted=2.1
>expected=-0.1, predicted=-0.0
>expected=-1.1, predicted=-0.9
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=0.0, predicted=-0.1
>expected=0.6, predicted=0.7
>expected=0.0, predicted=0.6
>expected=0.7, predicted=0.1
>expected=0.3, predicted=0.2
>expected=1.8, predicted=1.1
>expected=2.1, predicted=1.9
>expected=0.1, predicted=0.4
>expected=-0.2, predicted=-0.3
>expected=-1.2, predicted=-0.9
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-0.9
>expected=-0.1, predicted=-0.3
>expected=0.5, predicted=0.5
>expected=0.5, predicted=0.7
>expected=0.7, predicted=0.4
>expected=0.1, predicted=-0.0
>expected=-0.0, predicted=0.1
>expected=2.9, predicted=2.4
>expected=2.3, predicted=1.8
>expected=-0.9, predicted=-0.7
>expected=-0.9, predicted=-0.9
>expected=-1.0, predicted=-0.8
>expected=-1.0, predicted=-0.8
>expected=-0.4, predicted=-0.5
>expected=-0.0, predicted=0.0
>expected=1.6, predicted=0.9
>expected=1.2, predicted=0.7
>expected=-0.5, predicted=-0.4
>expected=1.6, predicted=1.4
>expected=1.8, predicted=1.8
>expected=-0.1, predicted=-0.3
>expected=-1.1, predicted=-1.0
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-0.9
>expected=0.0, predicted=0.2
>expected=0.6, predicted=0.5
>expected=0.0, predicted=0.7
>expected=0.7, predicted=0.5
>expected=0.3, predicted=0.1
>expected=1.8, predicted=0.9
>expected=2.1, predicted=1.7
>expected=0.1, predicted=0.9
>expected=-0.2, predicted=-0.2
>expected=-1.2, predicted=-0.9
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=-0.0
>expected=0.5, predicted=0.3
>expected=0.5, predicted=0.7
>expected=0.7, predicted=0.4
>expected=0.1, predicted=0.1
>expected=-0.0, predicted=0.0
>expected=2.9, predicted=2.3
>expected=2.3, predicted=2.1
>expected=-0.9, predicted=-0.8
>expected=-0.9, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-1.0, predicted=-0.9
>expected=-0.4, predicted=-0.2
>expected=-0.0, predicted=0.2
>expected=1.6, predicted=1.4
>expected=1.2, predicted=0.8
>expected=-0.5, predicted=-0.1
>expected=1.6, predicted=1.6
>expected=1.8, predicted=2.0
>expected=-0.1, predicted=-0.3
>expected=-1.1, predicted=-1.0
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-0.9
>expected=0.0, predicted=-0.1
>expected=0.6, predicted=0.5
>expected=0.0, predicted=-0.0
>expected=0.7, predicted=0.4
>expected=0.3, predicted=0.2
>expected=1.8, predicted=0.7
>expected=2.1, predicted=2.0
>expected=0.1, predicted=0.9
>expected=-0.2, predicted=-0.3
>expected=-1.2, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-0.9
>expected=-0.1, predicted=0.0
>expected=0.5, predicted=0.4
>expected=0.5, predicted=0.8
>expected=0.7, predicted=0.4
>expected=0.1, predicted=0.1
>expected=-0.0, predicted=0.2
>expected=2.9, predicted=2.0
>expected=2.3, predicted=2.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-1.0, predicted=-0.9
>expected=-0.4, predicted=-0.3
>expected=-0.0, predicted=-0.0
>expected=1.6, predicted=1.2
>expected=1.2, predicted=0.7
>expected=-0.5, predicted=-0.5
>expected=1.6, predicted=1.6
>expected=1.8, predicted=2.0
>expected=-0.1, predicted=-0.0
>expected=-1.1, predicted=-1.0
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-1.0
>expected=0.0, predicted=0.1
>expected=0.6, predicted=0.6
>expected=0.0, predicted=-0.0
>expected=0.7, predicted=0.8
>expected=0.3, predicted=0.3
>expected=1.8, predicted=1.1
>expected=2.1, predicted=1.5
>expected=0.1, predicted=1.1
>expected=-0.2, predicted=-0.3
>expected=-1.2, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=-0.2
>expected=0.5, predicted=0.5
>expected=0.5, predicted=1.0
>expected=0.7, predicted=0.7
>expected=0.1, predicted=0.4
>expected=-0.0, predicted=0.2
>expected=2.9, predicted=2.6
>expected=2.3, predicted=1.8
>expected=-0.9, predicted=-0.6
>expected=-0.9, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-0.4, predicted=-0.7
>expected=-0.0, predicted=0.1
>expected=1.6, predicted=1.5
>expected=1.2, predicted=1.0
>expected=-0.5, predicted=-0.0
>expected=1.6, predicted=1.3
>expected=1.8, predicted=2.1
>expected=-0.1, predicted=-0.0
>expected=-1.1, predicted=-0.9
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=0.0, predicted=-0.1
>expected=0.6, predicted=0.7
>expected=0.0, predicted=0.6
>expected=0.7, predicted=0.1
>expected=0.3, predicted=0.2
>expected=1.8, predicted=1.1
>expected=2.1, predicted=1.9
>expected=0.1, predicted=0.4
>expected=-0.2, predicted=-0.3
>expected=-1.2, predicted=-0.9
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-0.9
>expected=-0.1, predicted=-0.3
>expected=0.5, predicted=0.5
>expected=0.5, predicted=0.7
>expected=0.7, predicted=0.4
>expected=0.1, predicted=-0.0
>expected=-0.0, predicted=0.1
>expected=2.9, predicted=2.4
>expected=2.3, predicted=1.8
>expected=-0.9, predicted=-0.7
>expected=-0.9, predicted=-0.9
>expected=-1.0, predicted=-0.8
>expected=-1.0, predicted=-0.8
>expected=-0.4, predicted=-0.5
>expected=-0.0, predicted=0.0
>expected=1.6, predicted=0.8
>expected=1.2, predicted=0.6
>expected=-0.5, predicted=-0.0
>expected=1.6, predicted=1.4
>expected=1.8, predicted=1.9
>expected=-0.1, predicted=-0.3
>expected=-1.1, predicted=-1.0
>expected=-1.2, predicted=-1.0
>expected=-0.9, predicted=-0.9
>expected=-0.9, predicted=-1.0
>expected=0.0, predicted=0.0
>expected=0.6, predicted=0.7
>expected=0.0, predicted=0.4
>expected=0.7, predicted=0.6
>expected=0.3, predicted=0.1
>expected=1.8, predicted=1.0
>expected=2.1, predicted=1.5
>expected=0.1, predicted=0.9
>expected=-0.2, predicted=-0.3
>expected=-1.2, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=-0.0
>expected=0.5, predicted=0.4
>expected=0.5, predicted=0.6
>expected=0.7, predicted=0.6
>expected=0.1, predicted=0.3
>expected=-0.0, predicted=0.0
>expected=2.9, predicted=2.4
>expected=2.3, predicted=1.8
>expected=-0.9, predicted=-0.8
>expected=-0.9, predicted=-1.0
>expected=-1.0, predicted=-1.0
>expected=-1.0, predicted=-0.9
>expected=-0.4, predicted=-0.2
No description has been provided for this image

EVALUATE THE MODEL¶

In [ ]:
# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics = pd.DataFrame(metrics)
In [ ]:
metrics
Out[ ]:
Model MAE RMSE R-squared Nash-Sutcliffe Efficiency Willmott's Index of Agreement
0 LightGBM 0.214981 0.327180 0.913240 0.913240 0.974880
1 XGBoost 0.193561 0.298177 0.927940 0.927940 0.979673
2 SVM 0.221556 0.276759 0.937921 0.937921 0.982007
3 Random Forest 0.223784 0.321889 0.916024 0.916024 0.975080
In [ ]:
metrics = metrics.round(2)

fig, ax = plt.subplots(figsize=(20, 4))
ax.axis('off')

table = ax.table(cellText=metrics.values,
                 colLabels=metrics.columns,
                 cellLoc='center',
                 loc='center',
                 bbox=[0, 0, 1, 1])

table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.2, 1.2)

header_color = '#D0D0D0'
data_color = '#FFFFFF'

for i, key in enumerate(table._cells):
    cell = table._cells[key]
    cell.set_edgecolor('black')
    if key[0] == 0 or key[1] == -1:
        cell.set_text_props(weight='bold')
        cell.set_facecolor(header_color)
    else:
        cell.set_facecolor(data_color)

plt.title('Models Performance on Max Temperature (°C)')
plt.show()
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In [ ]:
colors = ['#27ad81', '#5dc863', '#aadc32', '#fde725']
bar_width = 0.4

# Metrics and Titles
metrics_title = {
    'MAE': 'Mean Absolute Error (MAE)',
    'RMSE': 'Root Mean Squared Error (RMSE)',
    'R-squared': 'R-squared',
    'Nash-Sutcliffe Efficiency': 'Nash-Sutcliffe Efficiency',
    'Willmott\'s Index of Agreement': 'Willmott\'s Index of Agreement'
}

# Plot each metric separately
for metric, title in metrics_title.items():
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.bar(metrics['Model'], metrics[metric], color=colors, width=bar_width)
    # ax.set_title(title, fontsize=16)
    ax.set_ylabel(metric, fontsize=16)
    ax.tick_params(axis='x', rotation=30, labelsize=16)
    ax.tick_params(axis='y', labelsize=16)
    plt.tight_layout()
    plt.show()
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8. TEMP MIN ANALYSIS¶

In [ ]:
# Get the temp_max values
temp_min = transformed_data[transformed_data['Parameter'] == parameters[2]]
temp_min.head()
Out[ ]:
Parameter Date Value
60 Min Temperature 1993-01-01 9.48
61 Min Temperature 1994-01-01 6.17
62 Min Temperature 1995-01-01 8.58
63 Min Temperature 1996-01-01 9.32
64 Min Temperature 1997-01-01 9.72
In [ ]:
# drop the parameter columns
temp_min = temp_min.drop(columns=['Parameter'])
In [ ]:
temp_min.head()
Out[ ]:
Date Value
60 1993-01-01 9.48
61 1994-01-01 6.17
62 1995-01-01 8.58
63 1996-01-01 9.32
64 1997-01-01 9.72
In [ ]:
# sort according to  the date
temp_min= temp_min.sort_values(by='Date')
temp_min.head()
Out[ ]:
Date Value
60 1993-01-01 9.48
270 1993-02-01 10.22
480 1993-03-01 9.40
690 1993-04-01 12.92
900 1993-05-01 12.01
In [ ]:
temp_min.head()
Out[ ]:
Date Value
60 1993-01-01 9.48
270 1993-02-01 10.22
480 1993-03-01 9.40
690 1993-04-01 12.92
900 1993-05-01 12.01
In [ ]:
plt.figure(figsize=(20, 8))
temp_min.plot(x='Date', y='Value', kind='line', ax=plt.gca(), color='#ff6347', fontsize=12)
plt.xlabel('Date', fontdict={'fontsize':20})
plt.ylabel('Min Temperature (°C)', fontdict={'fontsize':20})
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.grid(linestyle='--')
plt.tight_layout()
plt.legend().remove()
plt.show()
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In [ ]:
# Add fuller test to check the stationarity of the dataset
def adfuller_test(values):
    result = adfuller(values)
    labels = ['ADF Test Statistic','p-value','#Lags Used', 'Number of Observations Used']
    for value, label in zip(result, labels):
        print(label+' : '+str(value))
    if result[1] <= 0.05:
        print("Strong evidence against the null hypothesis(H0)")
    else:
        print("Weak evidence against the null hypothesis(H0)")
In [ ]:
# H0: It is non stationary
# H1: It is stationary
In [ ]:
adfuller_test(temp_min['Value'])

ADF Test Statistic : -5.08433457480597
p-value : 1.5072260094604089e-05
#Lags Used : 14
Number of Observations Used : 345
Strong evidence against the null hypothesis(H0)
In [ ]:
temp_min = temp_min[['Date', 'Value']]
temp_min.set_index('Date', inplace=True)
temp_min.head()
Out[ ]:
Value
Date
1993-01-01 9.48
1993-02-01 10.22
1993-03-01 9.40
1993-04-01 12.92
1993-05-01 12.01

TRAIN THE MODEL¶

In [ ]:
# Assuming `temp_min` is your time series DataFrame
series = temp_min
values = series.values
values = add_rolling_features(values, window=3)
values = pd.DataFrame(values).values
scaler = StandardScaler()
values = scaler.fit_transform(values)
data = series_to_supervised(values, n_in=6)
data_df = pd.DataFrame(data, index=series.index[6+2:])
In [ ]:
# Model Names
models = [
    ('LightGBM', lightgbm_forecast),
    ('XGBoost', xgboost_forecast),
    ('SVM', svm_forecast),
    ('Random Forest', random_forest_forecast)
]

# Clear existing lists in case they contain previous values
model_names.clear()
mae_values.clear()
rmse_values.clear()
r2_values.clear()
nse_values.clear()
willmott_values.clear()

# Iterate over each model, perform walk-forward validation, and append metrics to lists
for model_name, model_func in models:
    mae, rmse, r2, nse, willmott, test_index, y, yhat = walk_forward_validation(data_df, model_func)
    model_names.append(model_name)
    mae_values.append(mae)
    rmse_values.append(rmse)
    r2_values.append(r2)
    nse_values.append(nse)
    willmott_values.append(willmott)

# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics_df = pd.DataFrame(metrics)

# Plot the actual vs predicted values for each model
plt.figure(figsize=(16, 6))
plt.plot(test_index, y, label='Actual', linestyle='--', color='gray', linewidth=2)


# Plot predictions for each model
for model_name, model_func in models:
    _, _, _, _, _, test_index, _, yhat = walk_forward_validation(data_df, model_func)
    plt.plot(test_index, yhat, label=f'{model_name} Predicted', linestyle='--', color=colors_dict[model_name], linewidth=2)

plt.xlabel('Year and Month', fontsize=18)
plt.ylabel('Scaled Min Temperature (°C)', fontsize=18)
plt.legend(fontsize=14, loc='upper center', bbox_to_anchor=(0.6, 1.15), ncol=1)
plt.xticks(rotation=45, fontsize=14)
plt.yticks(fontsize=14)

plt.grid(axis='y', linestyle='--')

ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

>expected=-0.6, predicted=-0.8
>expected=0.4, predicted=0.1
>expected=0.5, predicted=0.2
>expected=-1.1, predicted=-0.7
>expected=0.2, predicted=-0.5
>expected=-0.2, predicted=-0.4
>expected=-0.2, predicted=-0.3
>expected=-0.9, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=0.6, predicted=0.2
>expected=0.9, predicted=1.1
>expected=-0.2, predicted=0.5
>expected=-0.6, predicted=-0.7
>expected=0.2, predicted=0.2
>expected=0.4, predicted=1.0
>expected=0.2, predicted=0.2
>expected=-0.6, predicted=-0.8
>expected=-1.1, predicted=-0.2
>expected=-0.6, predicted=-0.8
>expected=-0.6, predicted=-0.6
>expected=-0.9, predicted=-0.9
>expected=1.2, predicted=0.7
>expected=0.9, predicted=1.2
>expected=0.6, predicted=0.2
>expected=1.0, predicted=-0.5
>expected=1.0, predicted=0.7
>expected=0.7, predicted=1.0
>expected=1.6, predicted=0.7
>expected=-0.2, predicted=-0.4
>expected=-0.4, predicted=-0.9
>expected=-0.3, predicted=-0.5
>expected=-1.1, predicted=-0.8
>expected=-1.3, predicted=-1.1
>expected=-0.2, predicted=0.3
>expected=0.3, predicted=0.4
>expected=-0.8, predicted=-0.8
>expected=-0.6, predicted=-0.9
>expected=0.4, predicted=0.3
>expected=0.5, predicted=-0.5
>expected=-1.1, predicted=-0.8
>expected=0.2, predicted=-0.4
>expected=-0.2, predicted=-0.4
>expected=-0.2, predicted=-0.3
>expected=-0.9, predicted=-1.0
>expected=-1.2, predicted=-0.9
>expected=0.6, predicted=-0.1
>expected=0.9, predicted=0.9
>expected=-0.2, predicted=0.1
>expected=-0.6, predicted=-0.6
>expected=0.2, predicted=0.1
>expected=0.4, predicted=0.9
>expected=0.2, predicted=0.6
>expected=-0.6, predicted=-0.8
>expected=-1.1, predicted=-0.7
>expected=-0.6, predicted=-0.6
>expected=-0.6, predicted=-0.6
>expected=-0.9, predicted=-0.9
>expected=1.2, predicted=1.0
>expected=0.9, predicted=1.2
>expected=0.6, predicted=0.2
>expected=1.0, predicted=0.3
>expected=1.0, predicted=0.4
>expected=0.7, predicted=1.2
>expected=1.6, predicted=1.1
>expected=-0.2, predicted=-1.0
>expected=-0.4, predicted=-0.6
>expected=-0.3, predicted=-0.5
>expected=-1.1, predicted=-0.8
>expected=-1.3, predicted=-1.0
>expected=-0.2, predicted=0.0
>expected=0.3, predicted=0.2
>expected=-0.8, predicted=-0.6
>expected=-0.6, predicted=-0.4
>expected=0.4, predicted=-0.4
>expected=0.5, predicted=0.3
>expected=-1.1, predicted=0.0
>expected=0.2, predicted=-0.3
>expected=-0.2, predicted=-0.2
>expected=-0.2, predicted=-0.4
>expected=-0.9, predicted=-0.6
>expected=-1.2, predicted=-1.0
>expected=0.6, predicted=0.2
>expected=0.9, predicted=1.0
>expected=-0.2, predicted=0.1
>expected=-0.6, predicted=-0.3
>expected=0.2, predicted=0.3
>expected=0.4, predicted=1.0
>expected=0.2, predicted=0.5
>expected=-0.6, predicted=-0.6
>expected=-1.1, predicted=-0.9
>expected=-0.6, predicted=-0.7
>expected=-0.6, predicted=-0.8
>expected=-0.9, predicted=-1.0
>expected=1.2, predicted=0.8
>expected=0.9, predicted=1.1
>expected=0.6, predicted=0.1
>expected=1.0, predicted=0.5
>expected=1.0, predicted=0.9
>expected=0.7, predicted=1.1
>expected=1.6, predicted=1.1
>expected=-0.2, predicted=-0.8
>expected=-0.4, predicted=-1.0
>expected=-0.3, predicted=-0.7
>expected=-1.1, predicted=-0.7
>expected=-1.3, predicted=-1.0
>expected=-0.2, predicted=-0.3
>expected=0.3, predicted=0.5
>expected=-0.8, predicted=-0.3
>expected=-0.6, predicted=-0.5
>expected=0.4, predicted=0.3
>expected=0.5, predicted=0.4
>expected=-1.1, predicted=-0.5
>expected=0.2, predicted=-0.5
>expected=-0.2, predicted=-0.5
>expected=-0.2, predicted=-0.3
>expected=-0.9, predicted=-0.9
>expected=-1.2, predicted=-0.9
>expected=0.6, predicted=0.1
>expected=0.9, predicted=0.8
>expected=-0.2, predicted=0.3
>expected=-0.6, predicted=-0.2
>expected=0.2, predicted=0.2
>expected=0.4, predicted=1.1
>expected=0.2, predicted=0.8
>expected=-0.6, predicted=-0.8
>expected=-1.1, predicted=-0.7
>expected=-0.6, predicted=-0.7
>expected=-0.6, predicted=-0.7
>expected=-0.9, predicted=-1.0
>expected=1.2, predicted=1.0
>expected=0.9, predicted=1.1
>expected=0.6, predicted=0.3
>expected=1.0, predicted=-0.1
>expected=1.0, predicted=0.9
>expected=0.7, predicted=0.9
>expected=1.6, predicted=0.9
>expected=-0.2, predicted=-0.6
>expected=-0.4, predicted=-0.7
>expected=-0.3, predicted=-0.5
>expected=-1.1, predicted=-0.8
>expected=-1.3, predicted=-0.9
>expected=-0.2, predicted=0.1
>expected=0.3, predicted=0.3
>expected=-0.8, predicted=-0.2
>expected=-0.6, predicted=-0.8
>expected=0.4, predicted=0.1
>expected=0.5, predicted=0.2
>expected=-1.1, predicted=-0.7
>expected=0.2, predicted=-0.5
>expected=-0.2, predicted=-0.4
>expected=-0.2, predicted=-0.3
>expected=-0.9, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=0.6, predicted=0.2
>expected=0.9, predicted=1.1
>expected=-0.2, predicted=0.5
>expected=-0.6, predicted=-0.7
>expected=0.2, predicted=0.2
>expected=0.4, predicted=1.0
>expected=0.2, predicted=0.2
>expected=-0.6, predicted=-0.8
>expected=-1.1, predicted=-0.2
>expected=-0.6, predicted=-0.8
>expected=-0.6, predicted=-0.6
>expected=-0.9, predicted=-0.9
>expected=1.2, predicted=0.7
>expected=0.9, predicted=1.2
>expected=0.6, predicted=0.2
>expected=1.0, predicted=-0.5
>expected=1.0, predicted=0.7
>expected=0.7, predicted=1.0
>expected=1.6, predicted=0.7
>expected=-0.2, predicted=-0.4
>expected=-0.4, predicted=-0.9
>expected=-0.3, predicted=-0.5
>expected=-1.1, predicted=-0.8
>expected=-1.3, predicted=-1.1
>expected=-0.2, predicted=0.3
>expected=0.3, predicted=0.4
>expected=-0.8, predicted=-0.8
>expected=-0.6, predicted=-0.9
>expected=0.4, predicted=0.3
>expected=0.5, predicted=-0.5
>expected=-1.1, predicted=-0.8
>expected=0.2, predicted=-0.4
>expected=-0.2, predicted=-0.4
>expected=-0.2, predicted=-0.3
>expected=-0.9, predicted=-1.0
>expected=-1.2, predicted=-0.9
>expected=0.6, predicted=-0.1
>expected=0.9, predicted=0.9
>expected=-0.2, predicted=0.1
>expected=-0.6, predicted=-0.6
>expected=0.2, predicted=0.1
>expected=0.4, predicted=0.9
>expected=0.2, predicted=0.6
>expected=-0.6, predicted=-0.8
>expected=-1.1, predicted=-0.7
>expected=-0.6, predicted=-0.6
>expected=-0.6, predicted=-0.6
>expected=-0.9, predicted=-0.9
>expected=1.2, predicted=1.0
>expected=0.9, predicted=1.2
>expected=0.6, predicted=0.2
>expected=1.0, predicted=0.3
>expected=1.0, predicted=0.4
>expected=0.7, predicted=1.2
>expected=1.6, predicted=1.1
>expected=-0.2, predicted=-1.0
>expected=-0.4, predicted=-0.6
>expected=-0.3, predicted=-0.5
>expected=-1.1, predicted=-0.8
>expected=-1.3, predicted=-1.0
>expected=-0.2, predicted=0.0
>expected=0.3, predicted=0.2
>expected=-0.8, predicted=-0.6
>expected=-0.6, predicted=-0.4
>expected=0.4, predicted=-0.4
>expected=0.5, predicted=0.3
>expected=-1.1, predicted=0.0
>expected=0.2, predicted=-0.3
>expected=-0.2, predicted=-0.2
>expected=-0.2, predicted=-0.4
>expected=-0.9, predicted=-0.6
>expected=-1.2, predicted=-1.0
>expected=0.6, predicted=0.2
>expected=0.9, predicted=1.0
>expected=-0.2, predicted=0.1
>expected=-0.6, predicted=-0.3
>expected=0.2, predicted=0.3
>expected=0.4, predicted=1.0
>expected=0.2, predicted=0.5
>expected=-0.6, predicted=-0.6
>expected=-1.1, predicted=-0.9
>expected=-0.6, predicted=-0.7
>expected=-0.6, predicted=-0.8
>expected=-0.9, predicted=-1.0
>expected=1.2, predicted=0.8
>expected=0.9, predicted=1.1
>expected=0.6, predicted=0.1
>expected=1.0, predicted=0.5
>expected=1.0, predicted=0.9
>expected=0.7, predicted=1.1
>expected=1.6, predicted=1.1
>expected=-0.2, predicted=-0.8
>expected=-0.4, predicted=-1.0
>expected=-0.3, predicted=-0.7
>expected=-1.1, predicted=-0.7
>expected=-1.3, predicted=-1.0
>expected=-0.2, predicted=-0.3
>expected=0.3, predicted=0.5
>expected=-0.8, predicted=-0.3
>expected=-0.6, predicted=-0.5
>expected=0.4, predicted=0.3
>expected=0.5, predicted=0.4
>expected=-1.1, predicted=-0.4
>expected=0.2, predicted=-0.6
>expected=-0.2, predicted=-0.5
>expected=-0.2, predicted=-0.4
>expected=-0.9, predicted=-0.9
>expected=-1.2, predicted=-1.0
>expected=0.6, predicted=0.2
>expected=0.9, predicted=0.8
>expected=-0.2, predicted=0.4
>expected=-0.6, predicted=-0.3
>expected=0.2, predicted=0.2
>expected=0.4, predicted=0.7
>expected=0.2, predicted=0.6
>expected=-0.6, predicted=-0.8
>expected=-1.1, predicted=-0.7
>expected=-0.6, predicted=-0.8
>expected=-0.6, predicted=-0.8
>expected=-0.9, predicted=-1.0
>expected=1.2, predicted=1.1
>expected=0.9, predicted=1.1
>expected=0.6, predicted=0.2
>expected=1.0, predicted=0.1
>expected=1.0, predicted=0.6
>expected=0.7, predicted=1.1
>expected=1.6, predicted=0.9
>expected=-0.2, predicted=-0.5
>expected=-0.4, predicted=-0.8
>expected=-0.3, predicted=-0.4
>expected=-1.1, predicted=-0.8
>expected=-1.3, predicted=-1.0
>expected=-0.2, predicted=0.0
>expected=0.3, predicted=0.4
>expected=-0.8, predicted=-0.3
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EVALUATE THE MODEL¶

In [ ]:
# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}
metrics = pd.DataFrame(metrics)
In [ ]:
metrics
Out[ ]:
Model MAE RMSE R-squared Nash-Sutcliffe Efficiency Willmott's Index of Agreement
0 LightGBM 0.334111 0.454298 0.644947 0.644947 0.896572
1 XGBoost 0.318128 0.395044 0.731526 0.731526 0.923272
2 SVM 0.330095 0.406966 0.715078 0.715078 0.916226
3 Random Forest 0.317475 0.398528 0.726770 0.726770 0.917568
In [ ]:
metrics = metrics.round(2)

fig, ax = plt.subplots(figsize=(20, 4))
ax.axis('off')

table = ax.table(cellText=metrics.values,
                 colLabels=metrics.columns,
                 cellLoc='center',
                 loc='center',
                 bbox=[0, 0, 1, 1])

table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.2, 1.2)

header_color = '#D0D0D0'
data_color = '#FFFFFF'

for i, key in enumerate(table._cells):
    cell = table._cells[key]
    cell.set_edgecolor('black')
    if key[0] == 0 or key[1] == -1:
        cell.set_text_props(weight='bold')
        cell.set_facecolor(header_color)
    else:
        cell.set_facecolor(data_color)

plt.title('Models Performance on Min Temperature (°C)')
plt.show()
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In [ ]:
colors = ['#27ad81', '#5dc863', '#aadc32', '#fde725']
bar_width = 0.4

# Metrics and Titles
metrics_title = {
    'MAE': 'Mean Absolute Error (MAE)',
    'RMSE': 'Root Mean Squared Error (RMSE)',
    'R-squared': 'R-squared',
    'Nash-Sutcliffe Efficiency': 'Nash-Sutcliffe Efficiency',
    'Willmott\'s Index of Agreement': 'Willmott\'s Index of Agreement'
}

# Plot each metric separately
for metric, title in metrics_title.items():
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.bar(metrics['Model'], metrics[metric], color=colors, width=bar_width)
    # ax.set_title(title, fontsize=16)
    ax.set_ylabel(metric, fontsize=16)
    ax.tick_params(axis='x', rotation=30, labelsize=16)
    ax.tick_params(axis='y', labelsize=16)
    plt.tight_layout()
    plt.show()
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9. WIND SPEED MAX ANALYSIS¶

In [ ]:
# Get the wind_max values
wind_max = transformed_data[transformed_data['Parameter'] == parameters[3]]
wind_max.head()
Out[ ]:
Parameter Date Value
90 Max Windspeed 1993-01-01 3.20
91 Max Windspeed 1994-01-01 2.82
92 Max Windspeed 1995-01-01 2.42
93 Max Windspeed 1996-01-01 2.58
94 Max Windspeed 1997-01-01 2.84
In [ ]:
# drop the parameter columns
wind_max = wind_max.drop(columns=['Parameter'])
In [ ]:
wind_max.head()
Out[ ]:
Date Value
90 1993-01-01 3.20
91 1994-01-01 2.82
92 1995-01-01 2.42
93 1996-01-01 2.58
94 1997-01-01 2.84
In [ ]:
# sort according to  the date
wind_max= wind_max.sort_values(by='Date')
wind_max.head()
Out[ ]:
Date Value
90 1993-01-01 3.20
300 1993-02-01 2.99
510 1993-03-01 2.87
720 1993-04-01 2.76
930 1993-05-01 3.57
In [ ]:
wind_max.head()
Out[ ]:
Date Value
90 1993-01-01 3.20
300 1993-02-01 2.99
510 1993-03-01 2.87
720 1993-04-01 2.76
930 1993-05-01 3.57
In [ ]:
plt.figure(figsize=(20, 8))
wind_max.plot(x='Date', y='Value', kind='line', ax=plt.gca(), color='#8c564b', fontsize=12)
plt.xlabel('Date', fontdict={'fontsize':20})
plt.ylabel('Max Windspeed (m/s)', fontdict={'fontsize':20})
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.grid(linestyle='--')
plt.tight_layout()
plt.legend().remove()
plt.show()
No description has been provided for this image
In [ ]:
# Add fuller test to check the stationarity of the dataset
def adfuller_test(values):
    result = adfuller(values)
    labels = ['ADF Test Statistic','p-value','#Lags Used', 'Number of Observations Used']
    for value, label in zip(result, labels):
        print(label+' : '+str(value))
    if result[1] <= 0.05:
        print("Strong evidence against the null hypothesis(H0)")
    else:
        print("Weak evidence against the null hypothesis(H0)")
In [ ]:
# H0: It is non stationary
# H1: It is stationary
In [ ]:
adfuller_test(wind_max['Value'])

ADF Test Statistic : -15.128102921923738
p-value : 7.297414308602987e-28
#Lags Used : 0
Number of Observations Used : 359
Strong evidence against the null hypothesis(H0)
In [ ]:
wind_max = wind_max[['Date', 'Value']]
wind_max.set_index('Date', inplace=True)
wind_max.head()
Out[ ]:
Value
Date
1993-01-01 3.20
1993-02-01 2.99
1993-03-01 2.87
1993-04-01 2.76
1993-05-01 3.57

TRAIN THE MODEL¶

In [ ]:
# Assuming `wind_max` is your time series DataFrame
series = wind_max
values = series.values
values = add_rolling_features(values, window=3)
values = pd.DataFrame(values).values
scaler = StandardScaler()
values = scaler.fit_transform(values)
data = series_to_supervised(values, n_in=6)
data_df = pd.DataFrame(data, index=series.index[6+2:])
In [ ]:
# Model names
models = [
    ('LightGBM', lightgbm_forecast),
    ('XGBoost', xgboost_forecast),
    ('SVM', svm_forecast),
    ('Random Forest', random_forest_forecast)
]

# Clear existing lists in case they contain previous values
model_names.clear()
mae_values.clear()
rmse_values.clear()
r2_values.clear()
nse_values.clear()
willmott_values.clear()

# Iterate over each model, perform walk-forward validation, and append metrics to lists
for model_name, model_func in models:
    mae, rmse, r2, nse, willmott, test_index, y, yhat = walk_forward_validation(data_df, model_func)
    
    model_names.append(model_name)
    mae_values.append(mae)
    rmse_values.append(rmse)
    r2_values.append(r2)
    nse_values.append(nse)
    willmott_values.append(willmott)

# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics_df = pd.DataFrame(metrics)

# Plot the actual vs predicted values for each model
plt.figure(figsize=(16, 6))
plt.plot(test_index, y, label='Actual', linestyle='--', color='gray', linewidth=2)


# Plot predictions for each model
for model_name, model_func in models:
    _, _, _, _, _, test_index, _, yhat = walk_forward_validation(data_df, model_func)
    plt.plot(test_index, yhat, label=f'{model_name} Predicted', linestyle='--', color=colors_dict[model_name], linewidth=2)

plt.xlabel('Year and Month', fontsize=18)
plt.ylabel('Scaled Max Windspeed (m/s)', fontsize=18)
plt.legend(fontsize=14, loc='upper center', bbox_to_anchor=(0.6, 1.15), ncol=1)
plt.xticks(rotation=45, fontsize=14)
plt.yticks(fontsize=14)

plt.grid(axis='y', linestyle='--')

ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

>expected=0.5, predicted=0.4
>expected=1.0, predicted=0.9
>expected=-0.6, predicted=0.3
>expected=-0.8, predicted=-0.3
>expected=1.4, predicted=1.1
>expected=1.4, predicted=0.6
>expected=1.5, predicted=1.1
>expected=-0.9, predicted=0.7
>expected=-0.1, predicted=-0.7
>expected=-0.1, predicted=-0.4
>expected=-1.3, predicted=-1.1
>expected=-0.8, predicted=-0.9
>expected=-0.4, predicted=0.1
>expected=1.5, predicted=1.0
>expected=2.2, predicted=1.7
>expected=-1.0, predicted=1.4
>expected=0.6, predicted=0.3
>expected=-0.0, predicted=0.4
>expected=2.0, predicted=0.8
>expected=1.7, predicted=1.1
>expected=1.7, predicted=1.1
>expected=-1.0, predicted=0.3
>expected=-1.0, predicted=-0.8
>expected=-1.0, predicted=-0.9
>expected=-0.4, predicted=-0.5
>expected=-0.1, predicted=-0.1
>expected=2.8, predicted=1.3
>expected=1.6, predicted=1.0
>expected=1.2, predicted=1.2
>expected=0.5, predicted=-0.0
>expected=0.5, predicted=0.1
>expected=-0.4, predicted=-0.6
>expected=-0.2, predicted=0.2
>expected=0.6, predicted=0.2
>expected=0.9, predicted=0.7
>expected=-1.5, predicted=-0.8
>expected=0.5, predicted=0.9
>expected=1.0, predicted=0.6
>expected=-0.6, predicted=0.6
>expected=-0.8, predicted=-0.3
>expected=1.4, predicted=1.3
>expected=1.4, predicted=0.5
>expected=1.5, predicted=1.4
>expected=-0.9, predicted=0.8
>expected=-0.1, predicted=-0.3
>expected=-0.1, predicted=-0.3
>expected=-1.3, predicted=-0.7
>expected=-0.8, predicted=-0.8
>expected=-0.4, predicted=0.3
>expected=1.5, predicted=1.5
>expected=2.2, predicted=1.2
>expected=-1.0, predicted=1.0
>expected=0.6, predicted=-0.2
>expected=-0.0, predicted=0.5
>expected=2.0, predicted=1.0
>expected=1.7, predicted=0.9
>expected=1.7, predicted=1.0
>expected=-1.0, predicted=0.1
>expected=-1.0, predicted=-0.7
>expected=-1.0, predicted=-0.8
>expected=-0.4, predicted=-0.4
>expected=-0.1, predicted=-0.4
>expected=2.8, predicted=1.4
>expected=1.6, predicted=1.6
>expected=1.2, predicted=1.6
>expected=0.5, predicted=-0.1
>expected=0.5, predicted=-0.2
>expected=-0.4, predicted=0.1
>expected=-0.2, predicted=0.0
>expected=0.6, predicted=-0.1
>expected=0.9, predicted=1.3
>expected=-1.5, predicted=-0.6
>expected=0.5, predicted=0.2
>expected=1.0, predicted=0.9
>expected=-0.6, predicted=0.4
>expected=-0.8, predicted=0.2
>expected=1.4, predicted=0.9
>expected=1.4, predicted=0.8
>expected=1.5, predicted=1.3
>expected=-0.9, predicted=0.5
>expected=-0.1, predicted=-0.0
>expected=-0.1, predicted=0.1
>expected=-1.3, predicted=-0.2
>expected=-0.8, predicted=-0.8
>expected=-0.4, predicted=-0.7
>expected=1.5, predicted=1.1
>expected=2.2, predicted=2.0
>expected=-1.0, predicted=0.9
>expected=0.6, predicted=0.8
>expected=-0.0, predicted=0.8
>expected=2.0, predicted=1.4
>expected=1.7, predicted=1.3
>expected=1.7, predicted=1.2
>expected=-1.0, predicted=0.3
>expected=-1.0, predicted=-0.3
>expected=-1.0, predicted=-0.4
>expected=-0.4, predicted=0.1
>expected=-0.1, predicted=-0.2
>expected=2.8, predicted=1.6
>expected=1.6, predicted=1.4
>expected=1.2, predicted=1.3
>expected=0.5, predicted=0.2
>expected=0.5, predicted=0.3
>expected=-0.4, predicted=-0.2
>expected=-0.2, predicted=-0.0
>expected=0.6, predicted=-0.1
>expected=0.9, predicted=0.6
>expected=-1.5, predicted=-0.8
>expected=0.5, predicted=0.1
>expected=1.0, predicted=0.6
>expected=-0.6, predicted=0.5
>expected=-0.8, predicted=-0.1
>expected=1.4, predicted=0.9
>expected=1.4, predicted=0.7
>expected=1.5, predicted=0.8
>expected=-0.9, predicted=0.9
>expected=-0.1, predicted=-0.6
>expected=-0.1, predicted=-0.1
>expected=-1.3, predicted=-0.5
>expected=-0.8, predicted=-0.8
>expected=-0.4, predicted=0.1
>expected=1.5, predicted=0.8
>expected=2.2, predicted=1.0
>expected=-1.0, predicted=1.3
>expected=0.6, predicted=-0.2
>expected=-0.0, predicted=0.3
>expected=2.0, predicted=1.1
>expected=1.7, predicted=1.2
>expected=1.7, predicted=0.9
>expected=-1.0, predicted=0.3
>expected=-1.0, predicted=-0.5
>expected=-1.0, predicted=-0.5
>expected=-0.4, predicted=-0.4
>expected=-0.1, predicted=-0.4
>expected=2.8, predicted=1.4
>expected=1.6, predicted=2.1
>expected=1.2, predicted=1.2
>expected=0.5, predicted=-0.1
>expected=0.5, predicted=0.0
>expected=-0.4, predicted=-0.2
>expected=-0.2, predicted=0.1
>expected=0.6, predicted=-0.2
>expected=0.9, predicted=0.8
>expected=-1.5, predicted=-0.3
>expected=0.5, predicted=0.4
>expected=1.0, predicted=0.9
>expected=-0.6, predicted=0.3
>expected=-0.8, predicted=-0.3
>expected=1.4, predicted=1.1
>expected=1.4, predicted=0.6
>expected=1.5, predicted=1.1
>expected=-0.9, predicted=0.7
>expected=-0.1, predicted=-0.7
>expected=-0.1, predicted=-0.4
>expected=-1.3, predicted=-1.1
>expected=-0.8, predicted=-0.9
>expected=-0.4, predicted=0.1
>expected=1.5, predicted=1.0
>expected=2.2, predicted=1.7
>expected=-1.0, predicted=1.4
>expected=0.6, predicted=0.3
>expected=-0.0, predicted=0.4
>expected=2.0, predicted=0.8
>expected=1.7, predicted=1.1
>expected=1.7, predicted=1.1
>expected=-1.0, predicted=0.3
>expected=-1.0, predicted=-0.8
>expected=-1.0, predicted=-0.9
>expected=-0.4, predicted=-0.5
>expected=-0.1, predicted=-0.1
>expected=2.8, predicted=1.3
>expected=1.6, predicted=1.0
>expected=1.2, predicted=1.2
>expected=0.5, predicted=-0.0
>expected=0.5, predicted=0.1
>expected=-0.4, predicted=-0.6
>expected=-0.2, predicted=0.2
>expected=0.6, predicted=0.2
>expected=0.9, predicted=0.7
>expected=-1.5, predicted=-0.8
>expected=0.5, predicted=0.9
>expected=1.0, predicted=0.6
>expected=-0.6, predicted=0.6
>expected=-0.8, predicted=-0.3
>expected=1.4, predicted=1.3
>expected=1.4, predicted=0.5
>expected=1.5, predicted=1.4
>expected=-0.9, predicted=0.8
>expected=-0.1, predicted=-0.3
>expected=-0.1, predicted=-0.3
>expected=-1.3, predicted=-0.7
>expected=-0.8, predicted=-0.8
>expected=-0.4, predicted=0.3
>expected=1.5, predicted=1.5
>expected=2.2, predicted=1.2
>expected=-1.0, predicted=1.0
>expected=0.6, predicted=-0.2
>expected=-0.0, predicted=0.5
>expected=2.0, predicted=1.0
>expected=1.7, predicted=0.9
>expected=1.7, predicted=1.0
>expected=-1.0, predicted=0.1
>expected=-1.0, predicted=-0.7
>expected=-1.0, predicted=-0.8
>expected=-0.4, predicted=-0.4
>expected=-0.1, predicted=-0.4
>expected=2.8, predicted=1.4
>expected=1.6, predicted=1.6
>expected=1.2, predicted=1.6
>expected=0.5, predicted=-0.1
>expected=0.5, predicted=-0.2
>expected=-0.4, predicted=0.1
>expected=-0.2, predicted=0.0
>expected=0.6, predicted=-0.1
>expected=0.9, predicted=1.3
>expected=-1.5, predicted=-0.6
>expected=0.5, predicted=0.2
>expected=1.0, predicted=0.9
>expected=-0.6, predicted=0.4
>expected=-0.8, predicted=0.2
>expected=1.4, predicted=0.9
>expected=1.4, predicted=0.8
>expected=1.5, predicted=1.3
>expected=-0.9, predicted=0.5
>expected=-0.1, predicted=-0.0
>expected=-0.1, predicted=0.1
>expected=-1.3, predicted=-0.2
>expected=-0.8, predicted=-0.8
>expected=-0.4, predicted=-0.7
>expected=1.5, predicted=1.1
>expected=2.2, predicted=2.0
>expected=-1.0, predicted=0.9
>expected=0.6, predicted=0.8
>expected=-0.0, predicted=0.8
>expected=2.0, predicted=1.4
>expected=1.7, predicted=1.3
>expected=1.7, predicted=1.2
>expected=-1.0, predicted=0.3
>expected=-1.0, predicted=-0.3
>expected=-1.0, predicted=-0.4
>expected=-0.4, predicted=0.1
>expected=-0.1, predicted=-0.2
>expected=2.8, predicted=1.6
>expected=1.6, predicted=1.4
>expected=1.2, predicted=1.3
>expected=0.5, predicted=0.2
>expected=0.5, predicted=0.3
>expected=-0.4, predicted=-0.2
>expected=-0.2, predicted=-0.0
>expected=0.6, predicted=-0.1
>expected=0.9, predicted=0.6
>expected=-1.5, predicted=-0.8
>expected=0.5, predicted=0.2
>expected=1.0, predicted=0.3
>expected=-0.6, predicted=0.3
>expected=-0.8, predicted=-0.1
>expected=1.4, predicted=0.9
>expected=1.4, predicted=0.7
>expected=1.5, predicted=0.8
>expected=-0.9, predicted=0.8
>expected=-0.1, predicted=-0.5
>expected=-0.1, predicted=-0.1
>expected=-1.3, predicted=-0.3
>expected=-0.8, predicted=-0.8
>expected=-0.4, predicted=-0.2
>expected=1.5, predicted=1.1
>expected=2.2, predicted=1.1
>expected=-1.0, predicted=1.3
>expected=0.6, predicted=-0.2
>expected=-0.0, predicted=0.4
>expected=2.0, predicted=0.7
>expected=1.7, predicted=0.9
>expected=1.7, predicted=1.0
>expected=-1.0, predicted=0.4
>expected=-1.0, predicted=-0.4
>expected=-1.0, predicted=-0.7
>expected=-0.4, predicted=-0.6
>expected=-0.1, predicted=-0.4
>expected=2.8, predicted=1.7
>expected=1.6, predicted=1.8
>expected=1.2, predicted=1.1
>expected=0.5, predicted=0.0
>expected=0.5, predicted=0.2
>expected=-0.4, predicted=-0.0
>expected=-0.2, predicted=0.1
>expected=0.6, predicted=-0.4
>expected=0.9, predicted=0.9
>expected=-1.5, predicted=-0.3
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EVALUATE THE MODEL¶

In [ ]:
# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics = pd.DataFrame(metrics)
In [ ]:
metrics
Out[ ]:
Model MAE RMSE R-squared Nash-Sutcliffe Efficiency Willmott's Index of Agreement
0 LightGBM 0.550763 0.750441 0.557154 0.557154 0.831958
1 XGBoost 0.612111 0.768917 0.535081 0.535081 0.823825
2 SVM 0.536653 0.685804 0.630155 0.630155 0.855556
3 Random Forest 0.672048 0.839571 0.445714 0.445714 0.771035
In [ ]:
metrics = metrics.round(2)

fig, ax = plt.subplots(figsize=(20, 4))
ax.axis('off')

table = ax.table(cellText=metrics.values,
                 colLabels=metrics.columns,
                 cellLoc='center',
                 loc='center',
                 bbox=[0, 0, 1, 1])

table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.2, 1.2)

header_color = '#D0D0D0'
data_color = '#FFFFFF'

for i, key in enumerate(table._cells):
    cell = table._cells[key]
    cell.set_edgecolor('black')
    if key[0] == 0 or key[1] == -1:
        cell.set_text_props(weight='bold')
        cell.set_facecolor(header_color)
    else:
        cell.set_facecolor(data_color)

plt.title('Models Performance on Windspeed Max (m/s)')
plt.show()
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In [ ]:
colors = ['#27ad81', '#5dc863', '#aadc32', '#fde725']
bar_width = 0.4

# Metrics and Titles
metrics_title = {
    'MAE': 'Mean Absolute Error (MAE)',
    'RMSE': 'Root Mean Squared Error (RMSE)',
    'R-squared': 'R-squared',
    'Nash-Sutcliffe Efficiency': 'Nash-Sutcliffe Efficiency',
    'Willmott\'s Index of Agreement': 'Willmott\'s Index of Agreement'
}

# Plot each metric separately
for metric, title in metrics_title.items():
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.bar(metrics['Model'], metrics[metric], color=colors, width=bar_width)
    # ax.set_title(title, fontsize=16)
    ax.set_ylabel(metric, fontsize=16)
    ax.tick_params(axis='x', rotation=30, labelsize=16)
    ax.tick_params(axis='y', labelsize=16)
    plt.tight_layout()
    plt.show()
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10. WIND SPEED MIN ANALYSIS¶

In [ ]:
# Get the wind_max values
wind_min = transformed_data[transformed_data['Parameter'] == parameters[4]]
wind_min.head()
Out[ ]:
Parameter Date Value
120 Min Windspeed 1993-01-01 0.11
121 Min Windspeed 1994-01-01 0.04
122 Min Windspeed 1995-01-01 0.05
123 Min Windspeed 1996-01-01 0.06
124 Min Windspeed 1997-01-01 0.04
In [ ]:
# drop the parameter columns
wind_min = wind_min.drop(columns=['Parameter'])
In [ ]:
wind_min.head()
Out[ ]:
Date Value
120 1993-01-01 0.11
121 1994-01-01 0.04
122 1995-01-01 0.05
123 1996-01-01 0.06
124 1997-01-01 0.04
In [ ]:
# sort according to  the date
wind_min= wind_min.sort_values(by='Date')
wind_min.head()
Out[ ]:
Date Value
120 1993-01-01 0.11
330 1993-02-01 0.20
540 1993-03-01 0.03
750 1993-04-01 0.04
960 1993-05-01 0.05
In [ ]:
wind_min.head()
Out[ ]:
Date Value
120 1993-01-01 0.11
330 1993-02-01 0.20
540 1993-03-01 0.03
750 1993-04-01 0.04
960 1993-05-01 0.05
In [ ]:
plt.figure(figsize=(20, 8))
wind_min.plot(x='Date', y='Value', kind='line', ax=plt.gca(), color='#ff7f0e', fontsize=12)
plt.xlabel('Date', fontdict={'fontsize':20})
plt.ylabel('Min Windspeed (m/s)', fontdict={'fontsize':20})
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.grid(linestyle='--')
plt.tight_layout()
plt.legend().remove()
plt.show()
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In [ ]:
# Add fuller test to check the stationarity of the dataset
def adfuller_test(values):
    result = adfuller(values)
    labels = ['ADF Test Statistic','p-value','#Lags Used', 'Number of Observations Used']
    for value, label in zip(result, labels):
        print(label+' : '+str(value))
    if result[1] <= 0.05:
        print("Strong evidence against the null hypothesis(H0)")
    else:
        print("Weak evidence against the null hypothesis(H0)")
In [ ]:
# H0: It is non stationary
# H1: It is stationary
In [ ]:
adfuller_test(wind_min['Value'])

ADF Test Statistic : -4.406645220965239
p-value : 0.00028889697176914374
#Lags Used : 12
Number of Observations Used : 347
Strong evidence against the null hypothesis(H0)
In [ ]:
wind_min = wind_min[['Date', 'Value']]
wind_min.set_index('Date', inplace=True)
wind_min.head()
Out[ ]:
Value
Date
1993-01-01 0.11
1993-02-01 0.20
1993-03-01 0.03
1993-04-01 0.04
1993-05-01 0.05

TRAIN THE MODEL¶

In [ ]:
# Assuming `wind_min` is your time series DataFrame
series = wind_min
values = series.values
values = add_rolling_features(values, window=3)
values = pd.DataFrame(values).values
scaler = StandardScaler()
values = scaler.fit_transform(values)
data = series_to_supervised(values, n_in=6)
data_df = pd.DataFrame(data, index=series.index[6+2:])
In [ ]:
# Model names
models = [
    ('LightGBM', lightgbm_forecast),
    ('XGBoost', xgboost_forecast),
    ('SVM', svm_forecast),
    ('Random Forest', random_forest_forecast)
]

# Clear existing lists in case they contain previous values
model_names.clear()
mae_values.clear()
rmse_values.clear()
r2_values.clear()
nse_values.clear()
willmott_values.clear()

# Iterate over each model, perform walk-forward validation, and append metrics to lists
for model_name, model_func in models:
    mae, rmse, r2, nse, willmott, test_index, y, yhat = walk_forward_validation(data_df, model_func)
    
    model_names.append(model_name)
    mae_values.append(mae)
    rmse_values.append(rmse)
    r2_values.append(r2)
    nse_values.append(nse)
    willmott_values.append(willmott)

# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics_df = pd.DataFrame(metrics)

# Plot the actual vs predicted values for each model
plt.figure(figsize=(16, 6))
plt.plot(test_index, y, label='Actual', linestyle='--', color='gray', linewidth=2)


# Plot predictions for each model
for model_name, model_func in models:
    _, _, _, _, _, test_index, _, yhat = walk_forward_validation(data_df, model_func)
    plt.plot(test_index, yhat, label=f'{model_name} Predicted', linestyle='--', color=colors_dict[model_name], linewidth=2)

plt.xlabel('Year and Month', fontsize=18)
plt.ylabel('Scaled Min Windspeed (m/s)', fontsize=18)
plt.legend(fontsize=14, loc='upper center', bbox_to_anchor=(0.6, 1.15), ncol=1)
plt.xticks(rotation=45, fontsize=14)
plt.yticks(fontsize=14)

plt.grid(axis='y', linestyle='--')

ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

>expected=-0.7, predicted=-0.7
>expected=-0.9, predicted=-0.9
>expected=-0.5, predicted=-0.4
>expected=-0.4, predicted=-0.5
>expected=-1.1, predicted=-0.7
>expected=-0.1, predicted=-0.6
>expected=0.1, predicted=0.0
>expected=0.8, predicted=1.2
>expected=1.4, predicted=0.9
>expected=1.9, predicted=1.1
>expected=-0.3, predicted=-0.2
>expected=-0.3, predicted=-0.5
>expected=-0.6, predicted=-0.6
>expected=-0.2, predicted=-0.1
>expected=-0.1, predicted=-0.1
>expected=-0.1, predicted=-0.1
>expected=-0.7, predicted=-0.7
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.0
>expected=1.5, predicted=1.1
>expected=1.5, predicted=1.6
>expected=1.4, predicted=1.9
>expected=0.2, predicted=0.1
>expected=-0.3, predicted=-0.3
>expected=-0.6, predicted=-0.6
>expected=-0.6, predicted=-0.5
>expected=-0.6, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-0.8, predicted=-0.8
>expected=0.1, predicted=0.1
>expected=3.2, predicted=1.2
>expected=1.9, predicted=1.7
>expected=2.5, predicted=2.2
>expected=0.2, predicted=0.4
>expected=0.2, predicted=-0.3
>expected=0.5, predicted=0.0
>expected=-0.7, predicted=-0.6
>expected=-0.9, predicted=-0.7
>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.5
>expected=-1.1, predicted=-0.7
>expected=-0.1, predicted=-0.5
>expected=0.1, predicted=0.1
>expected=0.8, predicted=0.5
>expected=1.4, predicted=0.4
>expected=1.9, predicted=1.5
>expected=-0.3, predicted=-0.2
>expected=-0.3, predicted=-0.6
>expected=-0.6, predicted=-0.7
>expected=-0.2, predicted=-0.2
>expected=-0.1, predicted=-0.0
>expected=-0.1, predicted=-0.2
>expected=-0.7, predicted=-0.7
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.2
>expected=1.5, predicted=1.3
>expected=1.5, predicted=1.3
>expected=1.4, predicted=1.6
>expected=0.2, predicted=-0.0
>expected=-0.3, predicted=-0.3
>expected=-0.6, predicted=-0.7
>expected=-0.6, predicted=-0.6
>expected=-0.6, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-0.8, predicted=-1.0
>expected=0.1, predicted=0.1
>expected=3.2, predicted=2.0
>expected=1.9, predicted=2.2
>expected=2.5, predicted=2.3
>expected=0.2, predicted=0.7
>expected=0.2, predicted=0.4
>expected=0.5, predicted=0.4
>expected=-0.7, predicted=-0.5
>expected=-0.9, predicted=-0.7
>expected=-0.5, predicted=-0.7
>expected=-0.4, predicted=-0.6
>expected=-1.1, predicted=-0.8
>expected=-0.1, predicted=-0.4
>expected=0.1, predicted=0.1
>expected=0.8, predicted=0.8
>expected=1.4, predicted=1.0
>expected=1.9, predicted=1.6
>expected=-0.3, predicted=-0.2
>expected=-0.3, predicted=-0.6
>expected=-0.6, predicted=-0.5
>expected=-0.2, predicted=-0.2
>expected=-0.1, predicted=-0.2
>expected=-0.1, predicted=-0.1
>expected=-0.7, predicted=-0.7
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.1
>expected=1.5, predicted=1.3
>expected=1.5, predicted=1.5
>expected=1.4, predicted=1.6
>expected=0.2, predicted=-0.1
>expected=-0.3, predicted=-0.5
>expected=-0.6, predicted=-0.8
>expected=-0.6, predicted=-0.2
>expected=-0.6, predicted=-0.5
>expected=-0.8, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=0.1, predicted=-0.0
>expected=3.2, predicted=2.6
>expected=1.9, predicted=1.5
>expected=2.5, predicted=2.0
>expected=0.2, predicted=0.2
>expected=0.2, predicted=-0.1
>expected=0.5, predicted=-0.2
>expected=-0.7, predicted=-0.6
>expected=-0.9, predicted=-0.7
>expected=-0.5, predicted=-0.5
>expected=-0.4, predicted=-0.5
>expected=-1.1, predicted=-0.7
>expected=-0.1, predicted=-0.7
>expected=0.1, predicted=-0.1
>expected=0.8, predicted=0.7
>expected=1.4, predicted=0.7
>expected=1.9, predicted=1.3
>expected=-0.3, predicted=-0.3
>expected=-0.3, predicted=-0.6
>expected=-0.6, predicted=-0.6
>expected=-0.2, predicted=-0.3
>expected=-0.1, predicted=-0.1
>expected=-0.1, predicted=-0.2
>expected=-0.7, predicted=-0.8
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.1
>expected=1.5, predicted=1.2
>expected=1.5, predicted=1.2
>expected=1.4, predicted=1.2
>expected=0.2, predicted=-0.1
>expected=-0.3, predicted=-0.3
>expected=-0.6, predicted=-0.8
>expected=-0.6, predicted=-0.6
>expected=-0.6, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-0.8, predicted=-0.8
>expected=0.1, predicted=0.1
>expected=3.2, predicted=1.9
>expected=1.9, predicted=2.5
>expected=2.5, predicted=2.2
>expected=0.2, predicted=1.0
>expected=0.2, predicted=-0.1
>expected=0.5, predicted=0.5
>expected=-0.7, predicted=-0.7
>expected=-0.9, predicted=-0.9
>expected=-0.5, predicted=-0.4
>expected=-0.4, predicted=-0.5
>expected=-1.1, predicted=-0.7
>expected=-0.1, predicted=-0.6
>expected=0.1, predicted=0.0
>expected=0.8, predicted=1.2
>expected=1.4, predicted=0.9
>expected=1.9, predicted=1.1
>expected=-0.3, predicted=-0.2
>expected=-0.3, predicted=-0.5
>expected=-0.6, predicted=-0.6
>expected=-0.2, predicted=-0.1
>expected=-0.1, predicted=-0.1
>expected=-0.1, predicted=-0.1
>expected=-0.7, predicted=-0.7
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.0
>expected=1.5, predicted=1.1
>expected=1.5, predicted=1.6
>expected=1.4, predicted=1.9
>expected=0.2, predicted=0.1
>expected=-0.3, predicted=-0.3
>expected=-0.6, predicted=-0.6
>expected=-0.6, predicted=-0.5
>expected=-0.6, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-0.8, predicted=-0.8
>expected=0.1, predicted=0.1
>expected=3.2, predicted=1.2
>expected=1.9, predicted=1.7
>expected=2.5, predicted=2.2
>expected=0.2, predicted=0.4
>expected=0.2, predicted=-0.3
>expected=0.5, predicted=0.0
>expected=-0.7, predicted=-0.6
>expected=-0.9, predicted=-0.7
>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.5
>expected=-1.1, predicted=-0.7
>expected=-0.1, predicted=-0.5
>expected=0.1, predicted=0.1
>expected=0.8, predicted=0.5
>expected=1.4, predicted=0.4
>expected=1.9, predicted=1.5
>expected=-0.3, predicted=-0.2
>expected=-0.3, predicted=-0.6
>expected=-0.6, predicted=-0.7
>expected=-0.2, predicted=-0.2
>expected=-0.1, predicted=-0.0
>expected=-0.1, predicted=-0.2
>expected=-0.7, predicted=-0.7
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.2
>expected=1.5, predicted=1.3
>expected=1.5, predicted=1.3
>expected=1.4, predicted=1.6
>expected=0.2, predicted=-0.0
>expected=-0.3, predicted=-0.3
>expected=-0.6, predicted=-0.7
>expected=-0.6, predicted=-0.6
>expected=-0.6, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=-0.8, predicted=-1.0
>expected=0.1, predicted=0.1
>expected=3.2, predicted=2.0
>expected=1.9, predicted=2.2
>expected=2.5, predicted=2.3
>expected=0.2, predicted=0.7
>expected=0.2, predicted=0.4
>expected=0.5, predicted=0.4
>expected=-0.7, predicted=-0.5
>expected=-0.9, predicted=-0.7
>expected=-0.5, predicted=-0.7
>expected=-0.4, predicted=-0.6
>expected=-1.1, predicted=-0.8
>expected=-0.1, predicted=-0.4
>expected=0.1, predicted=0.1
>expected=0.8, predicted=0.8
>expected=1.4, predicted=1.0
>expected=1.9, predicted=1.6
>expected=-0.3, predicted=-0.2
>expected=-0.3, predicted=-0.6
>expected=-0.6, predicted=-0.5
>expected=-0.2, predicted=-0.2
>expected=-0.1, predicted=-0.2
>expected=-0.1, predicted=-0.1
>expected=-0.7, predicted=-0.7
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.1
>expected=1.5, predicted=1.3
>expected=1.5, predicted=1.5
>expected=1.4, predicted=1.6
>expected=0.2, predicted=-0.1
>expected=-0.3, predicted=-0.5
>expected=-0.6, predicted=-0.8
>expected=-0.6, predicted=-0.2
>expected=-0.6, predicted=-0.5
>expected=-0.8, predicted=-0.6
>expected=-0.8, predicted=-0.8
>expected=0.1, predicted=-0.0
>expected=3.2, predicted=2.6
>expected=1.9, predicted=1.5
>expected=2.5, predicted=2.0
>expected=0.2, predicted=0.2
>expected=0.2, predicted=-0.1
>expected=0.5, predicted=-0.2
>expected=-0.7, predicted=-0.6
>expected=-0.9, predicted=-0.7
>expected=-0.5, predicted=-0.4
>expected=-0.4, predicted=-0.5
>expected=-1.1, predicted=-0.7
>expected=-0.1, predicted=-0.7
>expected=0.1, predicted=-0.1
>expected=0.8, predicted=0.8
>expected=1.4, predicted=0.7
>expected=1.9, predicted=1.3
>expected=-0.3, predicted=-0.3
>expected=-0.3, predicted=-0.7
>expected=-0.6, predicted=-0.6
>expected=-0.2, predicted=-0.1
>expected=-0.1, predicted=-0.1
>expected=-0.1, predicted=-0.2
>expected=-0.7, predicted=-0.8
>expected=0.0, predicted=-0.1
>expected=-0.0, predicted=-0.1
>expected=1.5, predicted=1.5
>expected=1.5, predicted=0.8
>expected=1.4, predicted=1.3
>expected=0.2, predicted=-0.1
>expected=-0.3, predicted=-0.3
>expected=-0.6, predicted=-0.8
>expected=-0.6, predicted=-0.7
>expected=-0.6, predicted=-0.6
>expected=-0.8, predicted=-0.7
>expected=-0.8, predicted=-0.7
>expected=0.1, predicted=0.2
>expected=3.2, predicted=1.9
>expected=1.9, predicted=2.2
>expected=2.5, predicted=2.0
>expected=0.2, predicted=1.1
>expected=0.2, predicted=0.2
>expected=0.5, predicted=0.4
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EVALUATE THE MODEL¶

In [ ]:
# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics = pd.DataFrame(metrics)
In [ ]:
metrics
Out[ ]:
Model MAE RMSE R-squared Nash-Sutcliffe Efficiency Willmott's Index of Agreement
0 LightGBM 0.245672 0.427693 0.826176 0.826176 0.946055
1 XGBoost 0.227090 0.333936 0.894033 0.894033 0.969856
2 SVM 0.201170 0.261384 0.935076 0.935076 0.981375
3 Random Forest 0.238017 0.358948 0.877564 0.877564 0.964694
In [ ]:
metrics = metrics.round(2)

fig, ax = plt.subplots(figsize=(20, 4))
ax.axis('off')

table = ax.table(cellText=metrics.values,
                 colLabels=metrics.columns,
                 cellLoc='center',
                 loc='center',
                 bbox=[0, 0, 1, 1])

table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.2, 1.2)

header_color = '#D0D0D0'
data_color = '#FFFFFF'

for i, key in enumerate(table._cells):
    cell = table._cells[key]
    cell.set_edgecolor('black')
    if key[0] == 0 or key[1] == -1:
        cell.set_text_props(weight='bold')
        cell.set_facecolor(header_color)
    else:
        cell.set_facecolor(data_color)

plt.title('Models Performance on Min Windspeed (m/s)')
plt.show()
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In [ ]:
colors = ['#27ad81', '#5dc863', '#aadc32', '#fde725']
bar_width = 0.4

# Metrics and Titles
metrics_title = {
    'MAE': 'Mean Absolute Error (MAE)',
    'RMSE': 'Root Mean Squared Error (RMSE)',
    'R-squared': 'R-squared',
    'Nash-Sutcliffe Efficiency': 'Nash-Sutcliffe Efficiency',
    'Willmott\'s Index of Agreement': 'Willmott\'s Index of Agreement'
}

# Plot each metric separately
for metric, title in metrics_title.items():
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.bar(metrics['Model'], metrics[metric], color=colors, width=bar_width)
    # ax.set_title(title, fontsize=16)
    ax.set_ylabel(metric, fontsize=16)
    ax.tick_params(axis='x', rotation=30, labelsize=16)
    ax.tick_params(axis='y', labelsize=16)
    plt.tight_layout()
    plt.show()
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11. PRECIPITATION ANALYSIS¶

In [ ]:
# Get the Precipitation values
precipitation = transformed_data[transformed_data['Parameter'] == parameters[-2]]
precipitation.head()
Out[ ]:
Parameter Date Value
150 Precipitation 1993-01-01 73.83
151 Precipitation 1994-01-01 0.00
152 Precipitation 1995-01-01 5.27
153 Precipitation 1996-01-01 63.28
154 Precipitation 1997-01-01 31.64
In [ ]:
# drop the parameter columns
precipitation = precipitation.drop(columns=['Parameter'])
In [ ]:
precipitation.head()
Out[ ]:
Date Value
150 1993-01-01 73.83
151 1994-01-01 0.00
152 1995-01-01 5.27
153 1996-01-01 63.28
154 1997-01-01 31.64
In [ ]:
# sort according to  the date
precipitation =precipitation.sort_values(by='Date')
precipitation.head()
Out[ ]:
Date Value
150 1993-01-01 73.83
360 1993-02-01 52.73
570 1993-03-01 73.83
780 1993-04-01 300.59
990 1993-05-01 400.78
In [ ]:
precipitation.head()
Out[ ]:
Date Value
150 1993-01-01 73.83
360 1993-02-01 52.73
570 1993-03-01 73.83
780 1993-04-01 300.59
990 1993-05-01 400.78
In [ ]:
plt.figure(figsize=(20, 8))
precipitation.plot(x='Date', y='Value', kind='line', ax=plt.gca(), color='#1f77b4', fontsize=12)
plt.xlabel('Date', fontdict={'fontsize':20})
plt.ylabel('Precipitation (mm)', fontdict={'fontsize':20})
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.grid(linestyle='--')
plt.tight_layout()
plt.legend().remove()
plt.show()
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In [ ]:
# Add fuller test to check the stationarity of the dataset
def adfuller_test(values):
    result = adfuller(values)
    labels = ['ADF Test Statistic','p-value','#Lags Used', 'Number of Observations Used']
    for value, label in zip(result, labels):
        print(label+' : '+str(value))
    if result[1] <= 0.05:
        print("Strong evidence against the null hypothesis(H0)")
    else:
        print("Weak evidence against the null hypothesis(H0)")
In [ ]:
# H0: It is non stationary
# H1: It is stationary
In [ ]:
adfuller_test(precipitation['Value'])

ADF Test Statistic : -2.2217437135011067
p-value : 0.1984485973873053
#Lags Used : 11
Number of Observations Used : 348
Weak evidence against the null hypothesis(H0)
In [ ]:
precipitation = precipitation[['Date', 'Value']]
precipitation.set_index('Date', inplace=True)
precipitation.head()
Out[ ]:
Value
Date
1993-01-01 73.83
1993-02-01 52.73
1993-03-01 73.83
1993-04-01 300.59
1993-05-01 400.78

TRAIN THE MODEL¶

In [ ]:
# Assuming `precipitation` is your time series DataFrame
series = precipitation
values = series.values
values = add_rolling_features(values, window=3)
values = pd.DataFrame(values).values
scaler = StandardScaler()
values = scaler.fit_transform(values)
data = series_to_supervised(values, n_in=6)
data_df = pd.DataFrame(data, index=series.index[6+2:])
In [ ]:
# Model names
models = [
    ('LightGBM', lightgbm_forecast),
    ('XGBoost', xgboost_forecast),
    ('SVM', svm_forecast),
    ('Random Forest', random_forest_forecast)
]

# Clear existing lists in case they contain previous values
model_names.clear()
mae_values.clear()
rmse_values.clear()
r2_values.clear()
nse_values.clear()
willmott_values.clear()

# Iterate over each model, perform walk-forward validation, and append metrics to lists
for model_name, model_func in models:
    mae, rmse, r2, nse, willmott, test_index, y, yhat = walk_forward_validation(data_df, model_func)
    
    model_names.append(model_name)
    mae_values.append(mae)
    rmse_values.append(rmse)
    r2_values.append(r2)
    nse_values.append(nse)
    willmott_values.append(willmott)

# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics_df = pd.DataFrame(metrics)

# Plot the actual vs predicted values for each model
plt.figure(figsize=(16, 6))
plt.plot(test_index, y, label='Actual', linestyle='--', color='gray', linewidth=2)



# Plot predictions for each model
for model_name, model_func in models:
    _, _, _, _, _, test_index, _, yhat = walk_forward_validation(data_df, model_func)
    plt.plot(test_index, yhat, label=f'{model_name} Predicted', linestyle='--', color=colors_dict[model_name], linewidth=2)

plt.xlabel('Year and Month', fontsize=18)
plt.ylabel('Scaled Precipitation (mm)', fontsize=18)
plt.legend(fontsize=14, loc='upper center', bbox_to_anchor=(0.6, 1.15), ncol=1)
plt.xticks(rotation=45, fontsize=14)
plt.yticks(fontsize=14)

plt.grid(axis='y', linestyle='--')

ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

>expected=-0.7, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=0.4, predicted=0.2
>expected=1.5, predicted=1.7
>expected=0.0, predicted=0.6
>expected=-0.5, predicted=-0.2
>expected=-0.5, predicted=-0.3
>expected=-0.5, predicted=-0.2
>expected=-0.4, predicted=-0.6
>expected=1.7, predicted=0.8
>expected=1.3, predicted=1.1
>expected=-1.0, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-0.6
>expected=-0.6, predicted=-0.4
>expected=0.8, predicted=0.5
>expected=0.2, predicted=-0.1
>expected=0.5, predicted=-0.3
>expected=1.8, predicted=0.4
>expected=0.6, predicted=0.7
>expected=1.2, predicted=0.2
>expected=0.4, predicted=-0.1
>expected=0.1, predicted=-0.2
>expected=-1.3, predicted=-1.0
>expected=-1.2, predicted=-0.8
>expected=-1.2, predicted=-1.0
>expected=0.8, predicted=0.4
>expected=0.5, predicted=0.4
>expected=-0.2, predicted=-0.5
>expected=0.5, predicted=-0.1
>expected=-0.3, predicted=0.0
>expected=-0.4, predicted=0.1
>expected=-0.2, predicted=0.0
>expected=0.6, predicted=-0.1
>expected=-0.5, predicted=-0.6
>expected=-0.7, predicted=-0.8
>expected=-1.2, predicted=-1.1
>expected=0.4, predicted=0.3
>expected=1.5, predicted=1.1
>expected=0.0, predicted=0.5
>expected=-0.5, predicted=-0.6
>expected=-0.5, predicted=-0.6
>expected=-0.5, predicted=-0.5
>expected=-0.4, predicted=-0.7
>expected=1.7, predicted=1.3
>expected=1.3, predicted=1.3
>expected=-1.0, predicted=-0.9
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-0.8
>expected=-0.6, predicted=-0.6
>expected=0.8, predicted=0.9
>expected=0.2, predicted=-0.3
>expected=0.5, predicted=-0.1
>expected=1.8, predicted=0.5
>expected=0.6, predicted=0.2
>expected=1.2, predicted=0.5
>expected=0.4, predicted=-0.0
>expected=0.1, predicted=-0.3
>expected=-1.3, predicted=-1.0
>expected=-1.2, predicted=-1.1
>expected=-1.2, predicted=-1.1
>expected=0.8, predicted=0.8
>expected=0.5, predicted=0.4
>expected=-0.2, predicted=-0.5
>expected=0.5, predicted=0.6
>expected=-0.3, predicted=0.2
>expected=-0.4, predicted=0.1
>expected=-0.2, predicted=-0.1
>expected=0.6, predicted=-0.0
>expected=-0.5, predicted=-0.5
>expected=-0.7, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=0.4, predicted=0.1
>expected=1.5, predicted=1.8
>expected=0.0, predicted=0.5
>expected=-0.5, predicted=-0.6
>expected=-0.5, predicted=-0.5
>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.4
>expected=1.7, predicted=1.2
>expected=1.3, predicted=1.3
>expected=-1.0, predicted=-0.8
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-0.8
>expected=-1.1, predicted=-0.6
>expected=-0.6, predicted=-0.1
>expected=0.8, predicted=0.5
>expected=0.2, predicted=0.2
>expected=0.5, predicted=-0.1
>expected=1.8, predicted=0.1
>expected=0.6, predicted=0.6
>expected=1.2, predicted=0.7
>expected=0.4, predicted=0.1
>expected=0.1, predicted=-0.5
>expected=-1.3, predicted=-0.9
>expected=-1.2, predicted=-0.8
>expected=-1.2, predicted=-0.7
>expected=0.8, predicted=0.5
>expected=0.5, predicted=0.3
>expected=-0.2, predicted=-0.2
>expected=0.5, predicted=0.0
>expected=-0.3, predicted=0.1
>expected=-0.4, predicted=0.0
>expected=-0.2, predicted=-0.3
>expected=0.6, predicted=0.0
>expected=-0.5, predicted=-0.6
>expected=-0.7, predicted=-0.8
>expected=-1.2, predicted=-1.1
>expected=0.4, predicted=0.2
>expected=1.5, predicted=1.5
>expected=0.0, predicted=0.7
>expected=-0.5, predicted=-0.5
>expected=-0.5, predicted=-0.4
>expected=-0.5, predicted=-0.5
>expected=-0.4, predicted=-0.6
>expected=1.7, predicted=0.6
>expected=1.3, predicted=1.4
>expected=-1.0, predicted=-0.8
>expected=-1.1, predicted=-0.8
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-0.8
>expected=-0.6, predicted=-0.1
>expected=0.8, predicted=0.8
>expected=0.2, predicted=-0.1
>expected=0.5, predicted=-0.3
>expected=1.8, predicted=-0.0
>expected=0.6, predicted=0.5
>expected=1.2, predicted=0.0
>expected=0.4, predicted=0.2
>expected=0.1, predicted=-0.2
>expected=-1.3, predicted=-0.9
>expected=-1.2, predicted=-0.8
>expected=-1.2, predicted=-0.9
>expected=0.8, predicted=0.6
>expected=0.5, predicted=0.4
>expected=-0.2, predicted=-0.2
>expected=0.5, predicted=-0.2
>expected=-0.3, predicted=0.2
>expected=-0.4, predicted=-0.2
>expected=-0.2, predicted=-0.2
>expected=0.6, predicted=-0.1
>expected=-0.5, predicted=-0.6
>expected=-0.7, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=0.4, predicted=0.2
>expected=1.5, predicted=1.7
>expected=0.0, predicted=0.6
>expected=-0.5, predicted=-0.2
>expected=-0.5, predicted=-0.3
>expected=-0.5, predicted=-0.2
>expected=-0.4, predicted=-0.6
>expected=1.7, predicted=0.8
>expected=1.3, predicted=1.1
>expected=-1.0, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-0.6
>expected=-0.6, predicted=-0.4
>expected=0.8, predicted=0.5
>expected=0.2, predicted=-0.1
>expected=0.5, predicted=-0.3
>expected=1.8, predicted=0.4
>expected=0.6, predicted=0.7
>expected=1.2, predicted=0.2
>expected=0.4, predicted=-0.1
>expected=0.1, predicted=-0.2
>expected=-1.3, predicted=-1.0
>expected=-1.2, predicted=-0.8
>expected=-1.2, predicted=-1.0
>expected=0.8, predicted=0.4
>expected=0.5, predicted=0.4
>expected=-0.2, predicted=-0.5
>expected=0.5, predicted=-0.1
>expected=-0.3, predicted=0.0
>expected=-0.4, predicted=0.1
>expected=-0.2, predicted=0.0
>expected=0.6, predicted=-0.1
>expected=-0.5, predicted=-0.6
>expected=-0.7, predicted=-0.8
>expected=-1.2, predicted=-1.1
>expected=0.4, predicted=0.3
>expected=1.5, predicted=1.1
>expected=0.0, predicted=0.5
>expected=-0.5, predicted=-0.6
>expected=-0.5, predicted=-0.6
>expected=-0.5, predicted=-0.5
>expected=-0.4, predicted=-0.7
>expected=1.7, predicted=1.3
>expected=1.3, predicted=1.3
>expected=-1.0, predicted=-0.9
>expected=-1.1, predicted=-1.0
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-0.8
>expected=-0.6, predicted=-0.6
>expected=0.8, predicted=0.9
>expected=0.2, predicted=-0.3
>expected=0.5, predicted=-0.1
>expected=1.8, predicted=0.5
>expected=0.6, predicted=0.2
>expected=1.2, predicted=0.5
>expected=0.4, predicted=-0.0
>expected=0.1, predicted=-0.3
>expected=-1.3, predicted=-1.0
>expected=-1.2, predicted=-1.1
>expected=-1.2, predicted=-1.1
>expected=0.8, predicted=0.8
>expected=0.5, predicted=0.4
>expected=-0.2, predicted=-0.5
>expected=0.5, predicted=0.6
>expected=-0.3, predicted=0.2
>expected=-0.4, predicted=0.1
>expected=-0.2, predicted=-0.1
>expected=0.6, predicted=-0.0
>expected=-0.5, predicted=-0.5
>expected=-0.7, predicted=-1.0
>expected=-1.2, predicted=-1.2
>expected=0.4, predicted=0.1
>expected=1.5, predicted=1.8
>expected=0.0, predicted=0.5
>expected=-0.5, predicted=-0.6
>expected=-0.5, predicted=-0.5
>expected=-0.5, predicted=-0.6
>expected=-0.4, predicted=-0.4
>expected=1.7, predicted=1.2
>expected=1.3, predicted=1.3
>expected=-1.0, predicted=-0.8
>expected=-1.1, predicted=-1.1
>expected=-1.1, predicted=-0.8
>expected=-1.1, predicted=-0.6
>expected=-0.6, predicted=-0.1
>expected=0.8, predicted=0.5
>expected=0.2, predicted=0.2
>expected=0.5, predicted=-0.1
>expected=1.8, predicted=0.1
>expected=0.6, predicted=0.6
>expected=1.2, predicted=0.7
>expected=0.4, predicted=0.1
>expected=0.1, predicted=-0.5
>expected=-1.3, predicted=-0.9
>expected=-1.2, predicted=-0.8
>expected=-1.2, predicted=-0.7
>expected=0.8, predicted=0.5
>expected=0.5, predicted=0.3
>expected=-0.2, predicted=-0.2
>expected=0.5, predicted=0.0
>expected=-0.3, predicted=0.1
>expected=-0.4, predicted=0.0
>expected=-0.2, predicted=-0.3
>expected=0.6, predicted=0.0
>expected=-0.5, predicted=-0.6
>expected=-0.7, predicted=-0.7
>expected=-1.2, predicted=-1.1
>expected=0.4, predicted=0.2
>expected=1.5, predicted=1.4
>expected=0.0, predicted=0.9
>expected=-0.5, predicted=-0.4
>expected=-0.5, predicted=-0.4
>expected=-0.5, predicted=-0.5
>expected=-0.4, predicted=-0.6
>expected=1.7, predicted=0.8
>expected=1.3, predicted=1.3
>expected=-1.0, predicted=-0.9
>expected=-1.1, predicted=-0.8
>expected=-1.1, predicted=-0.9
>expected=-1.1, predicted=-0.8
>expected=-0.6, predicted=-0.2
>expected=0.8, predicted=0.7
>expected=0.2, predicted=-0.2
>expected=0.5, predicted=-0.2
>expected=1.8, predicted=0.3
>expected=0.6, predicted=0.3
>expected=1.2, predicted=0.0
>expected=0.4, predicted=0.0
>expected=0.1, predicted=-0.1
>expected=-1.3, predicted=-0.9
>expected=-1.2, predicted=-1.0
>expected=-1.2, predicted=-0.9
>expected=0.8, predicted=0.5
>expected=0.5, predicted=0.5
>expected=-0.2, predicted=-0.4
>expected=0.5, predicted=-0.1
>expected=-0.3, predicted=0.3
>expected=-0.4, predicted=-0.0
>expected=-0.2, predicted=-0.2
>expected=0.6, predicted=-0.1
>expected=-0.5, predicted=-0.6
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EVALUATE THE MODEL¶

In [ ]:
# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics = pd.DataFrame(metrics)
In [ ]:
metrics
Out[ ]:
Model MAE RMSE R-squared Nash-Sutcliffe Efficiency Willmott's Index of Agreement
0 LightGBM 0.359200 0.463300 0.718075 0.718075 0.903222
1 XGBoost 0.268449 0.376564 0.813754 0.813754 0.941519
2 SVM 0.322828 0.442464 0.742863 0.742863 0.913646
3 Random Forest 0.347592 0.507074 0.662284 0.662284 0.879269
In [ ]:
metrics = metrics.round(2)

fig, ax = plt.subplots(figsize=(20, 4))
ax.axis('off')

table = ax.table(cellText=metrics.values,
                 colLabels=metrics.columns,
                 cellLoc='center',
                 loc='center',
                 bbox=[0, 0, 1, 1])

table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.2, 1.2)

header_color = '#D0D0D0'
data_color = '#FFFFFF'

for i, key in enumerate(table._cells):
    cell = table._cells[key]
    cell.set_edgecolor('black')
    if key[0] == 0 or key[1] == -1:
        cell.set_text_props(weight='bold')
        cell.set_facecolor(header_color)
    else:
        cell.set_facecolor(data_color)

plt.title('Models Performance on Precipitation (mm)')
plt.show()
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In [ ]:
colors = ['#27ad81', '#5dc863', '#aadc32', '#fde725']
bar_width = 0.4

# Metrics and Titles
metrics_title = {
    'MAE': 'Mean Absolute Error (MAE)',
    'RMSE': 'Root Mean Squared Error (RMSE)',
    'R-squared': 'R-squared',
    'Nash-Sutcliffe Efficiency': 'Nash-Sutcliffe Efficiency',
    'Willmott\'s Index of Agreement': 'Willmott\'s Index of Agreement'
}

# Plot each metric separately
for metric, title in metrics_title.items():
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.bar(metrics['Model'], metrics[metric], color=colors, width=bar_width)
    # ax.set_title(title, fontsize=16)
    ax.set_ylabel(metric, fontsize=16)
    ax.tick_params(axis='x', rotation=30, labelsize=16)
    ax.tick_params(axis='y', labelsize=16)
    plt.tight_layout()
    plt.show()
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12. SOLAR RADIATION¶

In [ ]:
# Get the solar values
solar = transformed_data[transformed_data['Parameter'] == parameters[-1]]
solar.head()
Out[ ]:
Parameter Date Value
180 Solar Radiation 1993-01-01 5.91
181 Solar Radiation 1994-01-01 6.32
182 Solar Radiation 1995-01-01 6.18
183 Solar Radiation 1996-01-01 6.16
184 Solar Radiation 1997-01-01 6.17
In [ ]:
# drop the parameter columns
solar = solar.drop(columns=['Parameter'])
In [ ]:
solar.head()
Out[ ]:
Date Value
180 1993-01-01 5.91
181 1994-01-01 6.32
182 1995-01-01 6.18
183 1996-01-01 6.16
184 1997-01-01 6.17
In [ ]:
# sort according to  the date
solar =solar.sort_values(by='Date')
solar.head()
Out[ ]:
Date Value
180 1993-01-01 5.91
390 1993-02-01 6.68
600 1993-03-01 7.35
810 1993-04-01 7.62
1020 1993-05-01 7.33
In [ ]:
solar.head()
Out[ ]:
Date Value
180 1993-01-01 5.91
390 1993-02-01 6.68
600 1993-03-01 7.35
810 1993-04-01 7.62
1020 1993-05-01 7.33
In [ ]:
plt.figure(figsize=(20, 8))
solar.plot(x='Date', y='Value', kind='line', ax=plt.gca(), color='#9467bd', fontsize=12)
plt.xlabel('Date', fontdict={'fontsize':20})
plt.ylabel('Solar Radiation (kJ m⁻² month⁻¹)', fontdict={'fontsize':20})
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.grid(linestyle='--')
plt.tight_layout()
plt.legend().remove()
plt.show()
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In [ ]:
# Add fuller test to check the stationarity of the dataset
def adfuller_test(values):
    result = adfuller(values)
    labels = ['ADF Test Statistic','p-value','#Lags Used', 'Number of Observations Used']
    for value, label in zip(result, labels):
        print(label+' : '+str(value))
    if result[1] <= 0.05:
        print("Strong evidence against the null hypothesis(H0)")
    else:
        print("Weak evidence against the null hypothesis(H0)")
In [ ]:
# H0: It is non stationary
# H1: It is stationary
In [ ]:
adfuller_test(solar['Value'])

ADF Test Statistic : -2.325987352209106
p-value : 0.16372344995610594
#Lags Used : 13
Number of Observations Used : 346
Weak evidence against the null hypothesis(H0)
In [ ]:
solar = solar[['Date', 'Value']]
solar.set_index('Date', inplace=True)
solar.head()
Out[ ]:
Value
Date
1993-01-01 5.91
1993-02-01 6.68
1993-03-01 7.35
1993-04-01 7.62
1993-05-01 7.33

TRAIN THE MODEL¶

In [ ]:
# Assuming `solar` is your time series DataFrame
series = solar
values = series.values
values = add_rolling_features(values, window=3)
values = pd.DataFrame(values).values
scaler = StandardScaler()
values = scaler.fit_transform(values)
data = series_to_supervised(values, n_in=6)
data_df = pd.DataFrame(data, index=series.index[6+2:])
In [ ]:
# Model names
models = [
    ('LightGBM', lightgbm_forecast),
    ('XGBoost', xgboost_forecast),
    ('SVM', svm_forecast),
    ('Random Forest', random_forest_forecast)
]

# Clear existing lists in case they contain previous values
model_names.clear()
mae_values.clear()
rmse_values.clear()
r2_values.clear()
nse_values.clear()
willmott_values.clear()

# Iterate over each model, perform walk-forward validation, and append metrics to lists
for model_name, model_func in models:
    mae, rmse, r2, nse, willmott, test_index, y, yhat = walk_forward_validation(data_df, model_func)
    
    model_names.append(model_name)
    mae_values.append(mae)
    rmse_values.append(rmse)
    r2_values.append(r2)
    nse_values.append(nse)
    willmott_values.append(willmott)

# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics_df = pd.DataFrame(metrics)

# Plot the actual vs predicted values for each model
plt.figure(figsize=(16, 6))
plt.plot(test_index, y, label='Actual', linestyle='--', color='gray', linewidth=2)


# Plot predictions for each model
for model_name, model_func in models:
    _, _, _, _, _, test_index, _, yhat = walk_forward_validation(data_df, model_func)
    plt.plot(test_index, yhat, label=f'{model_name} Predicted', linestyle='--', color=colors_dict[model_name], linewidth=2)

plt.xlabel('Year and Month', fontsize=18)
plt.ylabel('Scaled Solar Radiation (kJ m⁻² month⁻¹)', fontsize=18)
plt.legend(fontsize=14, loc='upper center', bbox_to_anchor=(0.6, 1.15), ncol=1)
plt.xticks(rotation=45, fontsize=14)
plt.yticks(fontsize=14)

plt.grid(axis='y', linestyle='--')

ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.show()

>expected=-1.0, predicted=-0.8
>expected=0.9, predicted=0.7
>expected=1.3, predicted=1.8
>expected=-0.3, predicted=0.0
>expected=-1.4, predicted=-1.3
>expected=-0.3, predicted=-0.1
>expected=-0.4, predicted=-0.4
>expected=-0.8, predicted=-0.9
>expected=-0.5, predicted=-0.6
>expected=-1.0, predicted=-1.2
>expected=0.4, predicted=0.6
>expected=0.2, predicted=0.4
>expected=-1.1, predicted=-1.0
>expected=-0.5, predicted=-0.0
>expected=2.1, predicted=1.4
>expected=1.1, predicted=0.9
>expected=-1.2, predicted=-1.0
>expected=0.3, predicted=0.2
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.3
>expected=-0.5, predicted=-0.7
>expected=-1.2, predicted=-1.4
>expected=0.5, predicted=0.5
>expected=0.7, predicted=1.0
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=0.5
>expected=1.3, predicted=1.3
>expected=0.3, predicted=0.5
>expected=-1.1, predicted=-1.3
>expected=-0.2, predicted=-0.1
>expected=-1.0, predicted=-0.7
>expected=-0.5, predicted=-0.4
>expected=-1.0, predicted=-0.9
>expected=-1.1, predicted=-1.1
>expected=0.1, predicted=0.2
>expected=0.8, predicted=0.7
>expected=-1.0, predicted=-0.5
>expected=0.9, predicted=0.8
>expected=1.3, predicted=1.5
>expected=-0.3, predicted=-0.1
>expected=-1.4, predicted=-1.3
>expected=-0.3, predicted=-0.1
>expected=-0.4, predicted=-0.5
>expected=-0.8, predicted=-0.7
>expected=-0.5, predicted=-0.7
>expected=-1.0, predicted=-1.1
>expected=0.4, predicted=0.6
>expected=0.2, predicted=0.5
>expected=-1.1, predicted=-0.9
>expected=-0.5, predicted=-0.2
>expected=2.1, predicted=1.5
>expected=1.1, predicted=0.4
>expected=-1.2, predicted=-1.2
>expected=0.3, predicted=0.1
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.5
>expected=-0.5, predicted=-0.5
>expected=-1.2, predicted=-1.3
>expected=0.5, predicted=0.5
>expected=0.7, predicted=0.7
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=0.4
>expected=1.3, predicted=1.4
>expected=0.3, predicted=0.3
>expected=-1.1, predicted=-1.1
>expected=-0.2, predicted=0.1
>expected=-1.0, predicted=-0.5
>expected=-0.5, predicted=-0.4
>expected=-1.0, predicted=-0.9
>expected=-1.1, predicted=-1.2
>expected=0.1, predicted=0.4
>expected=0.8, predicted=0.9
>expected=-1.0, predicted=-0.8
>expected=0.9, predicted=1.0
>expected=1.3, predicted=1.6
>expected=-0.3, predicted=-0.1
>expected=-1.4, predicted=-1.3
>expected=-0.3, predicted=0.0
>expected=-0.4, predicted=-0.5
>expected=-0.8, predicted=-0.8
>expected=-0.5, predicted=-0.7
>expected=-1.0, predicted=-1.1
>expected=0.4, predicted=0.3
>expected=0.2, predicted=0.5
>expected=-1.1, predicted=-0.9
>expected=-0.5, predicted=0.1
>expected=2.1, predicted=1.5
>expected=1.1, predicted=0.9
>expected=-1.2, predicted=-1.3
>expected=0.3, predicted=0.3
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.6
>expected=-0.5, predicted=-0.4
>expected=-1.2, predicted=-1.2
>expected=0.5, predicted=0.6
>expected=0.7, predicted=0.7
>expected=-1.1, predicted=-0.9
>expected=-0.1, predicted=0.3
>expected=1.3, predicted=1.5
>expected=0.3, predicted=0.5
>expected=-1.1, predicted=-1.3
>expected=-0.2, predicted=0.1
>expected=-1.0, predicted=-0.7
>expected=-0.5, predicted=-1.0
>expected=-1.0, predicted=-0.8
>expected=-1.1, predicted=-1.0
>expected=0.1, predicted=0.3
>expected=0.8, predicted=0.3
>expected=-1.0, predicted=-0.7
>expected=0.9, predicted=0.7
>expected=1.3, predicted=1.4
>expected=-0.3, predicted=-0.0
>expected=-1.4, predicted=-1.2
>expected=-0.3, predicted=-0.1
>expected=-0.4, predicted=-0.4
>expected=-0.8, predicted=-0.9
>expected=-0.5, predicted=-0.6
>expected=-1.0, predicted=-1.1
>expected=0.4, predicted=0.6
>expected=0.2, predicted=0.5
>expected=-1.1, predicted=-1.0
>expected=-0.5, predicted=-0.1
>expected=2.1, predicted=1.4
>expected=1.1, predicted=0.6
>expected=-1.2, predicted=-1.0
>expected=0.3, predicted=0.2
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.6
>expected=-0.5, predicted=-0.6
>expected=-1.2, predicted=-1.2
>expected=0.5, predicted=0.6
>expected=0.7, predicted=0.9
>expected=-1.1, predicted=-1.1
>expected=-0.1, predicted=0.1
>expected=1.3, predicted=1.4
>expected=0.3, predicted=0.4
>expected=-1.1, predicted=-1.0
>expected=-0.2, predicted=0.0
>expected=-1.0, predicted=-0.7
>expected=-0.5, predicted=-0.6
>expected=-1.0, predicted=-1.0
>expected=-1.1, predicted=-1.1
>expected=0.1, predicted=0.5
>expected=0.8, predicted=0.9
>expected=-1.0, predicted=-0.8
>expected=0.9, predicted=0.7
>expected=1.3, predicted=1.8
>expected=-0.3, predicted=0.0
>expected=-1.4, predicted=-1.3
>expected=-0.3, predicted=-0.1
>expected=-0.4, predicted=-0.4
>expected=-0.8, predicted=-0.9
>expected=-0.5, predicted=-0.6
>expected=-1.0, predicted=-1.2
>expected=0.4, predicted=0.6
>expected=0.2, predicted=0.4
>expected=-1.1, predicted=-1.0
>expected=-0.5, predicted=-0.0
>expected=2.1, predicted=1.4
>expected=1.1, predicted=0.9
>expected=-1.2, predicted=-1.0
>expected=0.3, predicted=0.2
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.3
>expected=-0.5, predicted=-0.7
>expected=-1.2, predicted=-1.4
>expected=0.5, predicted=0.5
>expected=0.7, predicted=1.0
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=0.5
>expected=1.3, predicted=1.3
>expected=0.3, predicted=0.5
>expected=-1.1, predicted=-1.3
>expected=-0.2, predicted=-0.1
>expected=-1.0, predicted=-0.7
>expected=-0.5, predicted=-0.4
>expected=-1.0, predicted=-0.9
>expected=-1.1, predicted=-1.1
>expected=0.1, predicted=0.2
>expected=0.8, predicted=0.7
>expected=-1.0, predicted=-0.5
>expected=0.9, predicted=0.8
>expected=1.3, predicted=1.5
>expected=-0.3, predicted=-0.1
>expected=-1.4, predicted=-1.3
>expected=-0.3, predicted=-0.1
>expected=-0.4, predicted=-0.5
>expected=-0.8, predicted=-0.7
>expected=-0.5, predicted=-0.7
>expected=-1.0, predicted=-1.1
>expected=0.4, predicted=0.6
>expected=0.2, predicted=0.5
>expected=-1.1, predicted=-0.9
>expected=-0.5, predicted=-0.2
>expected=2.1, predicted=1.5
>expected=1.1, predicted=0.4
>expected=-1.2, predicted=-1.2
>expected=0.3, predicted=0.1
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.5
>expected=-0.5, predicted=-0.5
>expected=-1.2, predicted=-1.3
>expected=0.5, predicted=0.5
>expected=0.7, predicted=0.7
>expected=-1.1, predicted=-1.0
>expected=-0.1, predicted=0.4
>expected=1.3, predicted=1.4
>expected=0.3, predicted=0.3
>expected=-1.1, predicted=-1.1
>expected=-0.2, predicted=0.1
>expected=-1.0, predicted=-0.5
>expected=-0.5, predicted=-0.4
>expected=-1.0, predicted=-0.9
>expected=-1.1, predicted=-1.2
>expected=0.1, predicted=0.4
>expected=0.8, predicted=0.9
>expected=-1.0, predicted=-0.8
>expected=0.9, predicted=1.0
>expected=1.3, predicted=1.6
>expected=-0.3, predicted=-0.1
>expected=-1.4, predicted=-1.3
>expected=-0.3, predicted=0.0
>expected=-0.4, predicted=-0.5
>expected=-0.8, predicted=-0.8
>expected=-0.5, predicted=-0.7
>expected=-1.0, predicted=-1.1
>expected=0.4, predicted=0.3
>expected=0.2, predicted=0.5
>expected=-1.1, predicted=-0.9
>expected=-0.5, predicted=0.1
>expected=2.1, predicted=1.5
>expected=1.1, predicted=0.9
>expected=-1.2, predicted=-1.3
>expected=0.3, predicted=0.3
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.6
>expected=-0.5, predicted=-0.4
>expected=-1.2, predicted=-1.2
>expected=0.5, predicted=0.6
>expected=0.7, predicted=0.7
>expected=-1.1, predicted=-0.9
>expected=-0.1, predicted=0.3
>expected=1.3, predicted=1.5
>expected=0.3, predicted=0.5
>expected=-1.1, predicted=-1.3
>expected=-0.2, predicted=0.1
>expected=-1.0, predicted=-0.7
>expected=-0.5, predicted=-1.0
>expected=-1.0, predicted=-0.8
>expected=-1.1, predicted=-1.0
>expected=0.1, predicted=0.3
>expected=0.8, predicted=0.3
>expected=-1.0, predicted=-0.7
>expected=0.9, predicted=0.8
>expected=1.3, predicted=1.4
>expected=-0.3, predicted=-0.1
>expected=-1.4, predicted=-1.3
>expected=-0.3, predicted=-0.1
>expected=-0.4, predicted=-0.4
>expected=-0.8, predicted=-0.8
>expected=-0.5, predicted=-0.5
>expected=-1.0, predicted=-1.1
>expected=0.4, predicted=0.5
>expected=0.2, predicted=0.6
>expected=-1.1, predicted=-0.9
>expected=-0.5, predicted=-0.1
>expected=2.1, predicted=1.4
>expected=1.1, predicted=0.7
>expected=-1.2, predicted=-1.1
>expected=0.3, predicted=0.1
>expected=-0.2, predicted=-0.3
>expected=-0.4, predicted=-0.6
>expected=-0.5, predicted=-0.5
>expected=-1.2, predicted=-1.2
>expected=0.5, predicted=0.6
>expected=0.7, predicted=0.9
>expected=-1.1, predicted=-1.1
>expected=-0.1, predicted=0.1
>expected=1.3, predicted=1.4
>expected=0.3, predicted=0.5
>expected=-1.1, predicted=-1.2
>expected=-0.2, predicted=0.0
>expected=-1.0, predicted=-0.6
>expected=-0.5, predicted=-0.6
>expected=-1.0, predicted=-1.0
>expected=-1.1, predicted=-1.0
>expected=0.1, predicted=0.4
>expected=0.8, predicted=0.8
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EVALUATE THE MODEL¶

In [ ]:
# Create a DataFrame
metrics = {
    'Model': model_names,
    'MAE': mae_values,
    'RMSE': rmse_values,
    'R-squared': r2_values,
    'Nash-Sutcliffe Efficiency': nse_values,
    'Willmott\'s Index of Agreement': willmott_values
}

metrics = pd.DataFrame(metrics)
In [ ]:
metrics
Out[ ]:
Model MAE RMSE R-squared Nash-Sutcliffe Efficiency Willmott's Index of Agreement
0 LightGBM 0.190723 0.247614 0.915181 0.915181 0.978122
1 XGBoost 0.184161 0.262571 0.904625 0.904625 0.974123
2 SVM 0.206087 0.252387 0.911880 0.911880 0.977107
3 Random Forest 0.175734 0.224137 0.930502 0.930502 0.981284
In [ ]:
metrics = metrics.round(2)

fig, ax = plt.subplots(figsize=(20, 4))
ax.axis('off')

table = ax.table(cellText=metrics.values,
                 colLabels=metrics.columns,
                 cellLoc='center',
                 loc='center',
                 bbox=[0, 0, 1, 1])

table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.2, 1.2)

header_color = '#D0D0D0'
data_color = '#FFFFFF'

for i, key in enumerate(table._cells):
    cell = table._cells[key]
    cell.set_edgecolor('black')
    if key[0] == 0 or key[1] == -1:
        cell.set_text_props(weight='bold')
        cell.set_facecolor(header_color)
    else:
        cell.set_facecolor(data_color)

plt.title('Models Performance on Solar Radiation (kJ m⁻² month⁻¹)')
plt.show()
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In [ ]:
colors = ['#27ad81', '#5dc863', '#aadc32', '#fde725']
bar_width = 0.4

# Metrics and Titles
metrics_title = {
    'MAE': 'Mean Absolute Error (MAE)',
    'RMSE': 'Root Mean Squared Error (RMSE)',
    'R-squared': 'R-squared',
    'Nash-Sutcliffe Efficiency': 'Nash-Sutcliffe Efficiency',
    'Willmott\'s Index of Agreement': 'Willmott\'s Index of Agreement'
}

# Plot each metric separately
for metric, title in metrics_title.items():
    fig, ax = plt.subplots(figsize=(8, 6))
    ax.bar(metrics['Model'], metrics[metric], color=colors, width=bar_width)
    # ax.set_title(title, fontsize=16)
    ax.set_ylabel(metric, fontsize=16)
    ax.tick_params(axis='x', rotation=30, labelsize=16)
    ax.tick_params(axis='y', labelsize=16)
    plt.tight_layout()
    plt.show()
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The End¶